|
1
|
Gao Y, Chen X, Yang Q, Lasso A, Kolesov I, Pieper S, Kikinis R, Tannenbaum A, Zhu L. An effective and open source interactive 3D medical image segmentation solution. Sci Rep 2024; 14:29878. [PMID: 39622975 PMCID: PMC11612195 DOI: 10.1038/s41598-024-80206-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Accepted: 11/15/2024] [Indexed: 12/06/2024] Open
Abstract
3D medical image segmentation is a key step in numerous clinical applications. Even though many automatic segmentation solutions have been proposed, it is arguably that medical image segmentation is more of a preference than a reference as inter- and intra-variability are widely observed in final segmentation output. Therefore, designing a user oriented and open-source solution for interactive annotation is of great value for the community. In this paper, we present an effective interactive segmentation method that employs an adaptive dynamic programming approach to incorporates users' interactions efficiently. The method first initializes an segmentation through a feature-based geodesic computation. Then, the segmentation is further refined by using an efficient updating scheme requiring only local computations when new user inputs are available, making it applicable to high resolution images and very complex structures. The proposed method is implemented as a user-oriented software module in 3D Slicer. Our approach demonstrates several strengths and contributions. First, we proposed an efficient and effective 3D interactive algorithm with the adaptive dynamic programming method. Second, this is not just a presented algorithm, but also a software with well-designed GUI for users. Third, its open-source nature allows users to make customized modifications according to their specific requirements.
Collapse
Affiliation(s)
- Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China.
- Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen, 518060, China.
- Marshall Laboratory of Biomedical Engineering, Shenzhen, 518060, China.
- Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, 523000, Dongguan , China.
| | - Xiaohui Chen
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Qinzhu Yang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, Canada
| | - Ivan Kolesov
- Departments of Computer Science/Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | | | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Allen Tannenbaum
- Departments of Computer Science/Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Liangjia Zhu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30303, USA.
| |
Collapse
|
|
2
|
Horkaew P, Chansangrat J, Keeratibharat N, Le DC. Recent advances in computerized imaging and its vital roles in liver disease diagnosis, preoperative planning, and interventional liver surgery: A review. World J Gastrointest Surg 2023; 15:2382-2397. [PMID: 38111769 PMCID: PMC10725533 DOI: 10.4240/wjgs.v15.i11.2382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/30/2023] [Accepted: 09/27/2023] [Indexed: 11/26/2023] Open
Abstract
The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes. In clinical settings, screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering appropriate therapeutic procedures. Moreover, in a patient undergoing liver resection, a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments, making surgical decisions during the procedure, and anticipating postoperative results. Conventionally, various medical imaging modalities, e.g., computed tomography, magnetic resonance imaging, and positron emission tomography, have been employed to assist in these tasks. In fact, several standardized procedures, such as lesion detection and liver segmentation, are also incorporated into prominent commercial software packages. Thus far, most integrated software as a medical device typically involves tedious interactions from the physician, such as manual delineation and empirical adjustments, as per a given patient. With the rapid progress in digital health approaches, especially medical image analysis, a wide range of computer algorithms have been proposed to facilitate those procedures. They include pattern recognition of a liver, its periphery, and lesion, as well as pre- and postoperative simulations. Prior to clinical adoption, however, software must conform to regulatory requirements set by the governing agency, for instance, valid clinical association and analytical and clinical validation. Therefore, this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses, visualization, and simulation in the literature. Emphasis is placed upon their concepts, algorithmic classifications, merits, limitations, clinical considerations, and future research trends.
Collapse
Affiliation(s)
- Paramate Horkaew
- School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Jirapa Chansangrat
- School of Radiology, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Nattawut Keeratibharat
- School of Surgery, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand
| | - Doan Cong Le
- Faculty of Information Technology, An Giang University, Vietnam National University (Ho Chi Minh City), An Giang 90000, Vietnam
| |
Collapse
|
|
3
|
Lakshmipriya B, Pottakkat B, Ramkumar G. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artif Intell Med 2023; 141:102557. [PMID: 37295904 DOI: 10.1016/j.artmed.2023.102557] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 06/12/2023]
Abstract
Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.
Collapse
Affiliation(s)
- B Lakshmipriya
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Biju Pottakkat
- Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
| | - G Ramkumar
- Department of Radio Diagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| |
Collapse
|
|
4
|
Haseljić H, Chatterjee S, Frysch R, Kulvait V, Semshchikov V, Hensen B, Wacker F, Brüsch I, Werncke T, Speck O, Nürnberger A, Rose G. Liver segmentation using Turbolift learning for CT and cone-beam C-arm perfusion imaging. Comput Biol Med 2023; 154:106539. [PMID: 36689856 DOI: 10.1016/j.compbiomed.2023.106539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/30/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST - making the previous trainings act as pre-training stages for the subsequent ones - addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874±0.031 and 0.905±0.007 in 6-fold and 4-fold cross-validation experiments, respectively - securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
Collapse
Affiliation(s)
- Hana Haseljić
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany
| | - Soumick Chatterjee
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany; Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Genomics Research Centre, Human Technopole, Milan, Italy.
| | - Robert Frysch
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany
| | - Vojtěch Kulvait
- Institute of Materials Physics, Helmholtz-Zentrum hereon, Geesthacht, Germany
| | - Vladimir Semshchikov
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany
| | - Bennet Hensen
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Frank Wacker
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Inga Brüsch
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany
| | - Thomas Werncke
- Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Oliver Speck
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; German Center for Neurodegenerative Disease, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Andreas Nürnberger
- Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| | - Georg Rose
- Institute for Medical Engineering, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany
| |
Collapse
|
|
5
|
Jin R, Wang M, Xu L, Lu J, Song E, Ma G. Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour. Med Phys 2022; 50:2100-2120. [PMID: 36413182 DOI: 10.1002/mp.16116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 10/27/2022] [Accepted: 11/05/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods. METHODS The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues. RESULTS The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, - 0.02 % $-0.02\%$ , 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively. CONCLUSIONS The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods.
Collapse
Affiliation(s)
- Renchao Jin
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Manyang Wang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lijun Xu
- School of Computer and Information Engineering, Hubei University, Wuhan, China
| | - Jiayi Lu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guangzhi Ma
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
| |
Collapse
|
|
6
|
Chen Y, Zheng C, Hu F, Zhou T, Feng L, Xu G, Yi Z, Zhang X. Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field. Comput Biol Med 2022; 150:106076. [PMID: 36137320 DOI: 10.1016/j.compbiomed.2022.106076] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 11/24/2022]
Abstract
Segmentation of the liver and tumours from computed tomography (CT) scans is an important task in hepatic surgical planning. Manual segmentation of the liver and tumours is a time-consuming and labour-intensive task; therefore, a fully automated method for performing this segmentation is particularly desired. An automatic two-step liver and tumour segmentation method is presented in this paper. A cascade framework is used during the segmentation process, and a fully connected conditional random field (CRF) method is used to refine the tumour segmentation result. First, the proposed fractal residual U-Net (FRA-UNet) is used to locate and initially segment the liver. Then, FRA-UNet is further used to predict liver tumours from the liver region of interest (ROI). Finally, a three-dimensional (3D) CRF is used to refine the tumour segmentation results. The improved fractal residual (FR) structure effectively retains more effective features for improving the segmentation performance of deeper networks, the improved deep residual block can utilise the feature information more effectively, and the 3D CRF method smooths the contours and avoids the tumour oversegmentation problem. FRA-UNet is tested on the Liver Tumour Segmentation challenge dataset (LiTS) and the 3D Image Reconstruction for Comparison of Algorithm Database dataset (3DIRCADb), achieving 97.13% and 97.18% Dice similarity coefficients (DSCs) for liver segmentation and 71.78% and 68.97% DSCs for tumour segmentation, respectively, outperforming most state-of-the-art networks.
Collapse
Affiliation(s)
- Ying Chen
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Cheng Zheng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Fei Hu
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Taohui Zhou
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Longfeng Feng
- School of Software, Nanchang Hangkong University, Nanchang, 330063, China.
| | - Guohui Xu
- Department of Liver Neoplasms, Jiangxi Cancer Hospital, Nanchang, 330029, China.
| | - Zhen Yi
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, 330029, China.
| | - Xiang Zhang
- Wenzhou Data Management and Development Group Co.,Ltd, Wenzhou, Zhejiang, 325000, China.
| |
Collapse
|
|
7
|
Sorantin E, Grasser MG, Hemmelmayr A, Tschauner S, Hrzic F, Weiss V, Lacekova J, Holzinger A. The augmented radiologist: artificial intelligence in the practice of radiology. Pediatr Radiol 2022; 52:2074-2086. [PMID: 34664088 PMCID: PMC9537212 DOI: 10.1007/s00247-021-05177-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/03/2021] [Accepted: 08/02/2021] [Indexed: 12/19/2022]
Abstract
In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can "see" more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics - thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled "explainable AI," which should enable a balance/cooperation between AI and human intelligence - thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks.
Collapse
Affiliation(s)
- Erich Sorantin
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 36, A - 8036, Graz, Austria.
| | - Michael G Grasser
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 36, A - 8036, Graz, Austria
| | - Ariane Hemmelmayr
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 36, A - 8036, Graz, Austria
| | - Sebastian Tschauner
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 36, A - 8036, Graz, Austria
| | - Franko Hrzic
- Faculty of Engineering, Department of Computer Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia
| | - Veronika Weiss
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 36, A - 8036, Graz, Austria
| | - Jana Lacekova
- Division of Pediatric Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 36, A - 8036, Graz, Austria
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
| |
Collapse
|
|
8
|
Czipczer V, Manno-Kovacs A. Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
|
9
|
A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7954333. [PMID: 35755754 PMCID: PMC9225858 DOI: 10.1155/2022/7954333] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 12/24/2022]
Abstract
Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. Currently, many deep learning methods are used for liver segmentation that takes a long time to train the model which makes this task challenging and limited to larger hardware resources. In this research, we proposed a very lightweight convolutional neural network (CNN) to extract the liver region from CT scan images. The suggested CNN algorithm consists of 3 convolutional and 2 fully connected layers, where softmax is used to discriminate the liver from background. Random Gaussian distribution is used for weight initialization which achieved a distance-preserving-embedding of the information. The proposed network is known as Ga-CNN (Gaussian-weight initialization of CNN). General experiments are performed on three benchmark datasets including MICCAI SLiver’07, 3Dircadb01, and LiTS17. Experimental results show that the proposed method performed well on each benchmark dataset.
Collapse
|
|
10
|
A novel algorithm for refining cerebral vascular measurements in infants and adults. J Neurosci Methods 2020; 340:108751. [PMID: 32344044 DOI: 10.1016/j.jneumeth.2020.108751] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/03/2020] [Accepted: 04/21/2020] [Indexed: 01/11/2023]
Abstract
BACKGROUND Comprehensive quantification of intracranial vascular characteristics by vascular tracing provides an objective clinical assessment of vascular structure. However, weak signal or low contrast in small distal arteries, artifacts due to volitional motion, and vascular pulsation are challenges for accurate vessel tracing from 3D time-of-flight (3D-TOF) magnetic resonance angiography (MRA) images. NEW METHOD A vascular measurement refinement algorithm is developed and validated for robust quantification of intracranial vasculature from 3D-TOF MRA. After automated vascular tracing, centerline positions, lumen radii and centerline deviations are jointly optimized to restrict traces to within vascular regions in the straightened curved planar reformation (CPR) views. The algorithm is validated on simulated vascular images and on repeat 3D-TOF MRA acquired from infants and adults. RESULTS The refinement algorithm can reliably estimate vascular radius and correct deviated centerlines. For the simulated vascular image with noise level of 1 and deviation of centerline of 3, the mean radius difference is below 15.3 % for scan-rescan reliability. Vascular features from repeated clinical scans show significantly improved measurement agreement, with intra-class correlation coefficient (ICC) improvement from 0.55 to 0.7 for infants and from 0.59 to 0.92 for adults. COMPARISON WITH EXISTING METHODS The refinement algorithm is novel because it utilizes straightened CPR views that incorporate information from the entire artery. In addition, the optimization corrects centerline positions, lumen radii and centerline deviations simultaneously. CONCLUSIONS Intracranial vasculature quantification using a novel refinement algorithm for vascular tracing improves the reliability of vascular feature measurements in both infants and adults.
Collapse
|
|
11
|
Meng L, Tian Y, Bu S. Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys 2019; 21:144-157. [PMID: 31793212 PMCID: PMC6964770 DOI: 10.1002/acm2.12784] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 10/08/2019] [Accepted: 11/04/2019] [Indexed: 12/23/2022] Open
Abstract
Purpose Liver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The contrast between the liver tumors and healthy tissues in CT images is low and the boundary is blurred; (b) The image of liver tumor is complex and diversified in size, shape, and location. Methods To solve the above problems, this paper focused on the human liver and liver tumor segmentation algorithm based on convolutional neural network (CNN), and specially designed a three‐dimensional dual path multiscale convolutional neural network (TDP‐CNN). To balance the performance of segmentation and requirement of computational resources, the dual path was used in the network, then the feature maps from both paths were fused at the end of the paths. To refine the segmentation results, we used conditional random fields (CRF) to eliminate the false segmentation points in the segmentation results to improve the accuracy. Results In the experiment, we used the public dataset liver tumor segmentation (LiTS) to analyze the segmentation results qualitatively and quantitatively. Ground truth segmentation of liver and liver tumor was manually labeled by an experienced radiologist. Quantitative metrics were Dice, Hausdorff distance, and average distance. For the segmentation results of liver tumor, Dice was 0.689, Hausdorff distance was 7.69, and the average distance was 1.07; for the segmentation results of the liver, Dice was 0.965, Hausdorff distance was 29.162, and the average distance was 0.197. Compared with other liver and liver tumor segmentation algorithms in Medical Image Computing and Intervention (MICCAI) 2017 competition, our method of liver segmentation ranked first, and liver tumor segmentation ranked second. Conclusions The experimental results showed that the proposed algorithm had good performance in both liver and liver tumor segmentation.
Collapse
Affiliation(s)
- Lu Meng
- College of Information Science and Engineering, Northeastern University, ShenYang, China
| | - Yaoyu Tian
- College of Information Science and Engineering, Northeastern University, ShenYang, China
| | - Sihang Bu
- College of Information Science and Engineering, Northeastern University, ShenYang, China
| |
Collapse
|
|
12
|
3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review. J Digit Imaging 2019; 31:799-850. [PMID: 29915942 DOI: 10.1007/s10278-018-0101-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
Collapse
|
|
13
|
Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:6797102. [PMID: 30581550 PMCID: PMC6276449 DOI: 10.1155/2018/6797102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/30/2018] [Accepted: 10/22/2018] [Indexed: 11/18/2022]
Abstract
Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.
Collapse
|
|
14
|
Lu X, Xie Q, Zha Y, Wang D. Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images. Sci Rep 2018; 8:10700. [PMID: 30013150 PMCID: PMC6048104 DOI: 10.1038/s41598-018-28787-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 06/22/2018] [Indexed: 11/09/2022] Open
Abstract
Liver segmentation is an essential procedure in computer-assisted surgery, radiotherapy, and volume measurement. It is still a challenging task to extract liver tissue from 3D CT images owing to nearby organs with similar intensities. In this paper, an automatic approach integrating multi-dimensional features into graph cut refinement is developed and validated. Multi-atlas segmentation is utilized to estimate the coarse shape of liver on the target image. The unsigned distance field based on initial shape is then calculated throughout the whole image, which aims at automatic graph construction during refinement procedure. Finally, multi-dimensional features and shape constraints are embedded into graph cut framework. The optimal liver region can be precisely detected with a minimal cost. The proposed technique is evaluated on 40 CT scans, obtained from two public databases Sliver07 and 3Dircadb1. The dataset Sliver07 is considered as the training set for parameter learning. On the dataset 3Dircadb1, the average of volume overlap is up to 94%. The experiment results indicate that the proposed method has ability to reach the desired boundary of liver and has potential value for clinical application.
Collapse
Affiliation(s)
- Xuesong Lu
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, P. R. China
| | - Qinlan Xie
- College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074, P. R. China
| | - Yunfei Zha
- Department of Radiology, Remin Hospital of Wuhan University, Wuhan, 430060, P. R. China
| | - Defeng Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China. .,School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China. .,Research Centre for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
| |
Collapse
|
|
15
|
Huang Q, Ding H, Wang X, Wang G. Fully automatic liver segmentation in CT images using modified graph cuts and feature detection. Comput Biol Med 2018. [DOI: 10.1016/j.compbiomed.2018.02.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
|
16
|
Abstract
Medical image segmentation is a fundamental and challenging problem for analyzing medical images. Among different existing medical image segmentation methods, graph-based approaches are relatively new and show good features in clinical applications. In the graph-based method, pixels or regions in the original image are interpreted into nodes in a graph. By considering Markov random field to model the contexture information of the image, the medical image segmentation problem can be transformed into a graph-based energy minimization problem. This problem can be solved by the use of minimum s-t cut/ maximum flow algorithm. This review is devoted to cut-based medical segmentation methods, including graph cuts and graph search for region and surface segmentation. Different varieties of cut-based methods, including graph-cuts-based methods, model integrated graph cuts methods, graph-search-based methods, and graph search/graph cuts based methods, are systematically reviewed. Graph cuts and graph search with deep learning technique are also discussed.
Collapse
|
|
17
|
Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4D CT. Med Phys 2017; 45:297-306. [DOI: 10.1002/mp.12690] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 08/24/2017] [Accepted: 10/26/2017] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jungwon Cha
- Medical Imaging Laboratory; University of Louisville; Louisville KY 40292 USA
| | | | - Neal Dunlap
- Department of Radiation Oncology; Brown Cancer Center; University of Louisville; Louisville KY 40202 USA
| | - Amir A. Amini
- Medical Imaging Laboratory; University of Louisville; Louisville KY 40292 USA
| |
Collapse
|
|
18
|
Farhangi MM, Dunlap N, Amini A. Volumetric analysis of respiratory gated whole lung and liver CT data with motion-constrained graph cuts segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3405-3408. [PMID: 29060628 DOI: 10.1109/embc.2017.8037587] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The conventional graph cuts technique has been widely used for image segmentation due to its ability to find the global minimum and its ease of implementation. However, it is an intensity-based technique and as a result is limited to segmentation applications where there is significant contrast between the object and the background. We modified the conventional graph cuts method by adding shape prior and motion information. Active shape models (ASM) with signed distance functions were used to capture the shape prior information, preventing unwanted surrounding tissue from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend 3D segmentation to 4D by warping a prior shape model through time. The method has been applied to segmentation of whole lung boundary and whole liver boundary from respiratory gated CT data. 4D lung boundary segmentation in five patients, and 4D liver boundary segmentation in five patients were performed and in each case, results were compared with the results from expert-delineated ground truth. 4D segmentation for five phases of CT data took approximately ten minutes on a PC workstation with AMD Phenom II and 32GB of memory. An important by-product is quantitative whole organ volumes from respiratory gated CT from end-inspiration to end-expiration which can be determined with high accuracy.
Collapse
|
|
19
|
Patwardhan KA, Beichel RR, Smith BJ, Mart C, Plichta KA, Chang T, Sonka M, Graham MM, Magnotta V, Casavant T, Buatti JM. Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3409-3412. [PMID: 29060629 DOI: 10.1109/embc.2017.8037588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p <; 0.00001). TCP and UTCP presented no statistically significant differences (p ≥ 0.1884). The intra-operator standard deviations (SDs) for TCP, NTCP and UTCP were significantly lower for the semi-automatic segmentation method regardless of STAPLE design (p <; 0.0331). Both intra-and inter-operator SDs of TCP, NTCP and UTCP were significantly lower for semi-automatic segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.
Collapse
|
|
20
|
Beichel RR, Van Tol M, Ulrich EJ, Bauer C, Chang T, Plichta KA, Smith BJ, Sunderland JJ, Graham MM, Sonka M, Buatti JM. Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Med Phys 2017; 43:2948-2964. [PMID: 27277044 PMCID: PMC4874930 DOI: 10.1118/1.4948679] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Purpose: The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans. Methods: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the “just-enough-interaction” principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts. Results: Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach. Conclusions: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical oncology decision-making. The properties of the authors approach make it well suited for applications in image-guided radiation oncology, response assessment, or treatment outcome prediction.
Collapse
Affiliation(s)
- Reinhard R Beichel
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242; The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242; and Department of Internal Medicine, The University of Iowa, Iowa City, Iowa 52242
| | - Markus Van Tol
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242 and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242
| | - Ethan J Ulrich
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242 and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242
| | - Christian Bauer
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242 and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242
| | - Tangel Chang
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Kristin A Plichta
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Brian J Smith
- Department of Biostatistics, The University of Iowa, Iowa City, Iowa 52242
| | - John J Sunderland
- Department of Radiology, The University of Iowa, Iowa City, Iowa 52242
| | - Michael M Graham
- Department of Radiology, The University of Iowa, Iowa City, Iowa 52242
| | - Milan Sonka
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242; Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242; and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242
| | - John M Buatti
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242 and The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, Iowa 52242
| |
Collapse
|
|
21
|
Li D, Liu L, Chen J, Li H, Yin Y, Ibragimov B, Xing L. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours. Phys Med Biol 2017; 62:272-288. [PMID: 27991439 DOI: 10.1088/1361-6560/62/1/272] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.
Collapse
Affiliation(s)
- Dengwang Li
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Biomedical Sciences, School of Physics and Electronics, Shandong Normal University, Ji'nan, Shandong 250014, People's Republic of China. Medical Physics Division in the Department of Radiation Oncology, Stanford University, Palo Alto, CA 94305, USA
| | | | | | | | | | | | | |
Collapse
|
|
22
|
Kim Y, Bae SK, Cheng T, Tao C, Ge Y, Chapman AB, Torres VE, Yu ASL, Mrug M, Bennett WM, Flessner MF, Landsittel DP, Bae KT. Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease. Phys Med Biol 2016; 61:7864-7880. [PMID: 27779124 DOI: 10.1088/0031-9155/61/22/7864] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC = 0.91 for cyst volumes and ICC = 0.94 for % cyst-to-liver volume.
Collapse
Affiliation(s)
- Youngwoo Kim
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
|
23
|
Liao M, Zhao YQ, Wang W, Zeng YZ, Yang Q, Shih FY, Zou BJ. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 2016; 32:1383-1396. [PMID: 27771278 DOI: 10.1016/j.ejmp.2016.10.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 12/20/2022] Open
Abstract
Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.
Collapse
Affiliation(s)
- Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Wei Wang
- The Third Xiangya Hospital, Central South University, Changsha 410083, China.
| | - Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Qing Yang
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Frank Y Shih
- College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| |
Collapse
|
|
24
|
Peng J, Hu P, Lu F, Peng Z, Kong D, Zhang H. 3D liver segmentation using multiple region appearances and graph cuts. Med Phys 2016; 42:6840-52. [PMID: 26632041 DOI: 10.1118/1.4934834] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. METHODS The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. RESULTS The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. CONCLUSIONS A multiregion-appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state-of-the-art methods. Validation results suggest that it may be suitable for the clinical use.
Collapse
Affiliation(s)
- Jialin Peng
- College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
| | - Peijun Hu
- College of Mathematics, Zhejiang University, Hangzhou 310027, China
| | - Fang Lu
- College of Mathematics, Zhejiang University, Hangzhou 310027, China
| | - Zhiyi Peng
- Department of Radiology, First Affiliated Hospital, Zhejiang University, Hangzhou 310027, China
| | - Dexing Kong
- College of Mathematics, Zhejiang University, Hangzhou 310027, China
| | - Hongbo Zhang
- College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
| |
Collapse
|
|
25
|
Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 2016; 12:171-182. [PMID: 27604760 DOI: 10.1007/s11548-016-1467-3] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 07/26/2016] [Indexed: 12/12/2022]
Abstract
PURPOSE Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. METHODS The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. RESULTS The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. CONCLUSIONS The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.
Collapse
|
|
26
|
Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:9093721. [PMID: 27127536 PMCID: PMC4835633 DOI: 10.1155/2016/9093721] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Revised: 02/20/2016] [Accepted: 03/08/2016] [Indexed: 11/27/2022]
Abstract
Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.
Collapse
|
|
27
|
Li G, Chen X, Shi F, Zhu W, Tian J, Xiang D. Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:5315-5329. [PMID: 26415173 DOI: 10.1109/tip.2015.2481326] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Liver segmentation is still a challenging task in medical image processing area due to the complexity of the liver's anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.
Collapse
|
|
28
|
Abstract
To develop automatic and efficient liver contouring software for planning 3D-CT and four-dimensional computed tomography (4D-CT) for application in clinical radiation therapy treatment planning systems.The algorithm comprises three steps for overcoming the challenge of similar intensities between the liver region and its surrounding tissues. First, the total variation model with the L1 norm (TV-L1), which has the characteristic of multi-scale decomposition and an edge-preserving property, is used for removing the surrounding muscles and tissues. Second, an improved level set model that contains both global and local energy functions is utilized to extract liver contour information sequentially. In the global energy function, the local correlation coefficient (LCC) is constructed based on the gray level co-occurrence matrix both of the initial liver region and the background region. The LCC can calculate the correlation of a pixel with the foreground and background regions, respectively. The LCC is combined with intensity distribution models to classify pixels during the evolutionary process of the level set based method. The obtained liver contour is used as the candidate liver region for the following step. In the third step, voxel-based texture characterization is employed for refining the liver region and obtaining the final liver contours.The proposed method was validated based on the planning CT images of a group of 25 patients undergoing radiation therapy treatment planning. These included ten lung cancer patients with normal appearing livers and ten patients with hepatocellular carcinoma or liver metastases. The method was also tested on abdominal 4D-CT images of a group of five patients with hepatocellular carcinoma or liver metastases. The false positive volume percentage, the false negative volume percentage, and the dice similarity coefficient between liver contours obtained by a developed algorithm and a current standard delineated by the expert group are on an average 2.15-2.57%, 2.96-3.23%, and 91.01-97.21% for the CT images with normal appearing livers, 2.28-3.62%, 3.15-4.33%, and 86.14-93.53% for the CT images with hepatocellular carcinoma or liver metastases, and 2.37-3.96%, 3.25-4.57%, and 82.23-89.44% for the 4D-CT images also with hepatocellular carcinoma or liver metastases, respectively.The proposed three-step method can achieve efficient automatic liver contouring for planning CT and 4D-CT images with follow-up treatment planning and should find widespread applications in future treatment planning systems.
Collapse
Affiliation(s)
- Dengwang Li
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA. Medical Physics Research Center, School of Physics and Electronics, Shandong Normal University, Jinan, 250100, People's Republic of China
| | | | | | | |
Collapse
|
|
29
|
Saha PK, Strand R, Borgefors G. Digital Topology and Geometry in Medical Imaging: A Survey. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1940-1964. [PMID: 25879908 DOI: 10.1109/tmi.2015.2417112] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.
Collapse
|
|
30
|
Yuan Y, Chao M, Sheu RD, Rosenzweig K, Lo YC. Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT. Med Phys 2015; 42:4015-26. [PMID: 26133602 DOI: 10.1118/1.4922203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. METHODS In order to track the fuzzy liver-chestwall and liver-heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors' automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. RESULTS The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm(3), whereas it was 1719 cm(3) for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. CONCLUSIONS The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors' results also clearly demonstrated that the application of tracking the fuzzy borders could significantly reduce contour leakage during active contour evolution.
Collapse
Affiliation(s)
- Yading Yuan
- Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Ming Chao
- Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Ren-Dih Sheu
- Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Kenneth Rosenzweig
- Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Yeh-Chi Lo
- Department of Radiation Oncology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| |
Collapse
|
|
31
|
Suzuki K, Huynh HT, Liu Y, Calabrese D, Zhou K, Oto A, Hori M. Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:2984-7. [PMID: 24110354 DOI: 10.1109/embc.2013.6610167] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Computerized liver volumetry has been studied, because the current "gold-standard" manual volumetry is subjective and very time-consuming. Liver volumetry is done in either CT or MRI. A number of researchers have developed computerized liver segmentation in CT, but there are fewer studies on ones for MRI. Our purpose in this study was to develop a general framework for liver segmentation in both CT and MRI. Our scheme consisted of 1) an anisotropic diffusion filter to reduce noise while preserving liver structures, 2) a scale-specific gradient magnitude filter to enhance liver boundaries, 3) a fast-marching algorithm to roughly determine liver boundaries, and 4) a geodesic-active-contour model coupled with a level-set algorithm to refine the initial boundaries. Our CT database contained hepatic CT scans of 18 liver donors obtained under a liver transplant protocol. Our MRI database contains 23 patients with 1.5T MRI scanners. To establish "gold-standard" liver volumes, radiologists manually traced the contour of the liver on each CT or MR slice. We compared our computer volumetry with "gold-standard" manual volumetry. Computer volumetry in CT and MRI reached excellent agreement with manual volumetry (intra-class correlation coefficient = 0.94 and 0.98, respectively). Average user time for computer volumetry in CT and MRI was 0.57 ± 0.06 and 1.0 ± 0.13 min. per case, respectively, whereas those for manual volumetry were 39.4 ± 5.5 and 24.0 ± 4.4 min. per case, respectively, with statistically significant difference (p < .05). Our computerized liver segmentation framework provides an efficient and accurate way of measuring liver volumes in both CT and MRI.
Collapse
|
|
32
|
Yan Z, Zhang S, Tan C, Qin H, Belaroussi B, Yu HJ, Miller C, Metaxas DN. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials. Comput Med Imaging Graph 2015; 41:80-92. [PMID: 24962337 DOI: 10.1016/j.compmedimag.2014.05.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2014] [Revised: 03/30/2014] [Accepted: 05/29/2014] [Indexed: 01/14/2023]
Affiliation(s)
- Zhennan Yan
- CBIM, Rutgers University, Piscataway, NJ, USA
| | - Shaoting Zhang
- Department of Computer Science, UNC Charlotte, Charlotte, NC, USA.
| | - Chaowei Tan
- CBIM, Rutgers University, Piscataway, NJ, USA
| | - Hongxing Qin
- Chongqing University of Posts & Telecommunications, Chongqing, China
| | | | | | | | | |
Collapse
|
|
33
|
Tang S, Guo Y, Wang Y, Cao W, Sun F. Adaptive Cosegmentation of Pheochromocytomas in CECT Images Using Localized Level Set Models. IEEE J Biomed Health Inform 2015; 20:549-62. [PMID: 25680219 DOI: 10.1109/jbhi.2015.2402173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Segmentation of pheochromocytomas in contrast-enhanced computed tomography (CECT) images is an ill-posed problem due to the presence of weak boundaries, intratumoral degeneration, and nearby structures and clutter. Additional information from different phases of CECT images needs to be imposed for better mass segmentations. In this paper, a novel adaptive cosegmentation method is proposed by incorporating a localized region-based level set model (LRLSM). The energy function is formulated with consideration of adaptive tradeoff between the complementary local information from image pairs. Gradient direction and shape dissimilarity measure are integrated to guide the level set evolution. Automatic localization radius selection is added to further facilitate the segmentation. Then, two level set functions from each image pair are evolved and refined alternately to minimize the energy function. Experimental results in 50 CECT image pairs show that the adaptive LRLSM-based method is effective in segmentation of pheochromocytoma at two phases and produces better results, especially in the cases with weak boundaries, and complex foreground and background.
Collapse
|
|
34
|
Lu D, Wu Y, Harris G, Cai W. Iterative mesh transformation for 3D segmentation of livers with cancers in CT images. Comput Med Imaging Graph 2015; 43:1-14. [PMID: 25728595 DOI: 10.1016/j.compmedimag.2015.01.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 11/20/2014] [Accepted: 01/09/2015] [Indexed: 01/26/2023]
Abstract
Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.
Collapse
Affiliation(s)
- Difei Lu
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Department of Informatics, Zhejiang Police College, China
| | - Yin Wu
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Gordon Harris
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA.
| |
Collapse
|
|
35
|
Zhou J, Huang W, Chang SKY, Xiong W, Oo T, Chen W. Longitudinal in-vivo volumetry study for porcine liver regeneration from CT data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:4743-6. [PMID: 25571052 DOI: 10.1109/embc.2014.6944684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The use of hepatic-like cloned cord lining epithelial cells (CLEC) to enhance liver regeneration has been proposed, but has not been properly investigated in a large animal study. The paper presents a system developed for the longitudinal in-vivo volumetry study on porcine liver regeneration from computed tomography (CT) data. In this system, a rough 3D liver volume is firstly automatically segmented by a 3D mesh deformation-based method. Then a refinement step to eliminate the segmentation error is carried out by a 3D post-editing tool, followed by mesh-volume conversion and volume calculation. This system was applied in a pilot study, which was composed of 4/4 pigs in the Experimental/Control Groups, to measure liver volumes over pre- to post-operative time course. Experimental results suggest that (1) the developed system can perform CT-based porcine liver volumetry efficiently, and (2) the infusion of CLEC to liver remnant may potentially enhance the liver regeneration.
Collapse
|
|
36
|
Pathways and barriers to developing pediatric research consortia. Pediatr Radiol 2014; 44:1213-7. [PMID: 25246191 DOI: 10.1007/s00247-014-3055-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 05/12/2014] [Indexed: 10/24/2022]
Abstract
In many countries pediatric radiology is responsible for imaging of patients ranging from premature babies to 19-year-olds, thus meaning a weight range from less than 500 g to sometimes much more than 100 kg. Children are not small adults and many differences exist when comparing them to adults. Therefore pediatric radiologists face a 4-D problem: the three dimensions in space as well as time, which can be summarized as maturation. Due to this multidimensionality experience exchange, knowledge transfer and teamwork is mandatory. Research consortia represent a useful approach. According the author's experience in setting up and running international, inter-institutional academic networks, this article describes pathways and barriers of such cooperations, which can be divided into external (institutional), internal (departmental) and organizational and by the research fellows themselves.
Collapse
|
|
37
|
Kockelkorn TTJP, Schaefer-Prokop CM, Bozovic G, Muñoz-Barrutia A, van Rikxoort EM, Brown MS, de Jong PA, Viergever MA, van Ginneken B. Interactive lung segmentation in abnormal human and animal chest CT scans. Med Phys 2014; 41:081915. [DOI: 10.1118/1.4890597] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
|
|
38
|
Zhou W, Xie Y. Interactive contour delineation and refinement in treatment planning of image-guided radiation therapy. J Appl Clin Med Phys 2014; 15:4499. [PMID: 24423846 PMCID: PMC5711244 DOI: 10.1120/jacmp.v15i1.4499] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 08/22/2013] [Accepted: 07/15/2013] [Indexed: 11/23/2022] Open
Abstract
The accurate contour delineation of the target and/or organs at risk (OAR) is essential in treatment planning for image-guided radiation therapy (IGRT). Although many automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. Moreover, clinicians would like to analyze the characteristics of regions of interests (ROI) and adjust contours manually during IGRT. Interactive tool for contour delineation is necessary in such cases. In this work, a novel approach of curve fitting for interactive contour delineation is proposed. It allows users to quickly improve contours by a simple mouse click. Initially, a region which contains interesting object is selected in the image, then the program can automatically select important control points from the region boundary, and the method of Hermite cubic curves is used to fit the control points. Hence, the optimized curve can be revised by moving its control points interactively. Meanwhile, several curve fitting methods are presented for the comparison. Finally, in order to improve the accuracy of contour delineation, the process of the curve refinement based on the maximum gradient magnitude is proposed. All the points on the curve are revised automatically towards the positions with maximum gradient magnitude. Experimental results show that Hermite cubic curves and the curve refinement based on the maximum gradient magnitude possess superior performance on the proposed platform in terms of accuracy, robustness, and time calculation. Experimental results of real medical images demonstrate the efficiency, accuracy, and robustness of the proposed process in clinical applications.
Collapse
Affiliation(s)
- Wu Zhou
- Shenzhen Institutes of Advanced Technology.
| | | |
Collapse
|
|
39
|
Wang G, Zhang S, Li F, Gu L. A new segmentation framework based on sparse shape composition in liver surgery planning system. Med Phys 2013; 40:051913. [PMID: 23635283 DOI: 10.1118/1.4802215] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system. METHODS The segmentation framework proposed in this paper includes two important modules: (1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer. RESULTS Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively. CONCLUSIONS The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.
Collapse
Affiliation(s)
- Guotai Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | | | | | | |
Collapse
|
|
40
|
Changyang Li, Xiuying Wang, Eberl S, Fulham M, Yong Yin, Jinhu Chen, Feng DD. A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes. IEEE Trans Biomed Eng 2013; 60:2967-77. [DOI: 10.1109/tbme.2013.2267212] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
|
41
|
Sun S, Sonka M, Beichel RR. Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface. Comput Med Imaging Graph 2013; 37:15-27. [PMID: 23415254 DOI: 10.1016/j.compmedimag.2013.01.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2012] [Revised: 10/19/2012] [Accepted: 01/18/2013] [Indexed: 11/25/2022]
Abstract
Recently, the optimal surface finding (OSF) and layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) approaches have been reported with applications to medical image segmentation tasks. While providing high levels of performance, these approaches may locally fail in the presence of pathology or other local challenges. Due to the image data variability, finding a suitable cost function that would be applicable to all image locations may not be feasible. This paper presents a new interactive refinement approach for correcting local segmentation errors in the automated OSF-based segmentation. A hybrid desktop/virtual reality user interface was developed for efficient interaction with the segmentations utilizing state-of-the-art stereoscopic visualization technology and advanced interaction techniques. The user interface allows a natural and interactive manipulation of 3-D surfaces. The approach was evaluated on 30 test cases from 18 CT lung datasets, which showed local segmentation errors after employing an automated OSF-based lung segmentation. The performed experiments exhibited significant increase in performance in terms of mean absolute surface distance errors (2.54±0.75 mm prior to refinement vs. 1.11±0.43 mm post-refinement, p≪0.001). Speed of the interactions is one of the most important aspects leading to the acceptance or rejection of the approach by users expecting real-time interaction experience. The average algorithm computing time per refinement iteration was 150 ms, and the average total user interaction time required for reaching complete operator satisfaction was about 2 min per case. This time was mostly spent on human-controlled manipulation of the object to identify whether additional refinement was necessary and to approve the final segmentation result. The reported principle is generally applicable to segmentation problems beyond lung segmentation in CT scans as long as the underlying segmentation utilizes the OSF framework. The two reported segmentation refinement tools were optimized for lung segmentation and might need some adaptation for other application domains.
Collapse
Affiliation(s)
- Shanhui Sun
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, United States
| | | | | |
Collapse
|