Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Bobo MF, Bao S, Huo Y, Yao Y, Virostko J, Plassard AJ, Lyu I, Assad A, Abramson RG, Hilmes MA, Landman BA. Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation. Proc SPIE Int Soc Opt Eng 2018;10574:105742V. [PMID: 29887665 PMCID: PMC5992909 DOI: 10.1117/12.2293751] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]

For:	Bobo MF, Bao S, Huo Y, Yao Y, Virostko J, Plassard AJ, Lyu I, Assad A, Abramson RG, Hilmes MA, Landman BA. Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation. Proc SPIE Int Soc Opt Eng 2018;10574:105742V. [PMID: 29887665 PMCID: PMC5992909 DOI: 10.1117/12.2293751] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]

Number

Cited by Other Article(s)

Zhang H, Li H, Ali R, Jia W, Pan W, Reeder SB, Harris D, Masch W, Aslam A, Shanbhogue K, Parikh NA, Dillman JR, He L. Development and Validation of a Modality-Invariant 3D Swin U-Net Transformer for Liver and Spleen Segmentation on Multi-Site Clinical Bi-parametric MR Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01362-w. [PMID: 39707114 DOI: 10.1007/s10278-024-01362-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Revised: 11/26/2024] [Accepted: 11/28/2024] [Indexed: 12/23/2024]

Abstract

To develop and validate a modality-invariant Swin U-Net Transformer (UNETR) deep learning model for liver and spleen segmentation on abdominal T1-weighted (T1w) or T2-weighted (T2w) MR images from multiple institutions for pediatric and adult patients with known or suspected chronic liver diseases. In this IRB-approved retrospective study, clinical abdominal axial T1w and T2w MR images from pediatric and adult patients were retrieved from four study sites, including Cincinnati Children's Hospital Medical Center (CCHMC), New York University (NYU), University of Wisconsin (UW) and University of Michigan / Michigan Medicine (UM). The whole liver and spleen were manually delineated as the ground truth masks. We developed a modality-invariant 3D Swin UNETR using a modality-invariant training strategy, in which each patient's T1w and T2w MR images were treated as separate training samples. We conducted both internal and external validation experiments. A total of 241 T1w and 339 T2w MR sequences from 304 patients (age [mean ± standard deviation], 31.8 ± 20.3 years; 132 [43%] female) were included for model development. The Swin UNETR achieved a Dice similarity coefficient (DSC) of 0.95 ± 0.02 on T1w images and 0.93 ± 0.05 on T2w images for liver segmentation. This is significantly better than the U-Net model (0.90 ± 0.05, p < 0.001 and 0.90 ± 0.13, p < 0.001, respectively). The Swin UNETR achieved a DSC of 0.88 ± 0.12 on T1w images and 0.93 ± 0.10 on T2w images for spleen segmentation, and it significantly outperformed a modality-invariant U-Net model (0.80 ± 0.18, p = 0.001 and 0.88 ± 0.12, p = 0.002, respectively). Our study demonstrated that a modality-invariant Swin UNETR model can segment the liver and spleen on routinely collected clinical bi-parametric abdominal MR images from children and adult patients.

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Affiliation(s)

Huixian Zhang Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
Hailong Li Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
Redha Ali Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
Wei Jia Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Department of Biostatistics, University of Cincinnati, Cincinnati, OH, USA
Wen Pan Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
Scott B Reeder Department of Radiology, Medical Physics, Biomedical Engineering, Medicine, Emergency Medicine, University of Wisconsin, Madison, WI, USA
David Harris Department of Radiology, Medical Physics, Biomedical Engineering, Medicine, Emergency Medicine, University of Wisconsin, Madison, WI, USA
William Masch Department of Radiology, Michigan Medicine, Ann Arbor, MI, USA
Anum Aslam Department of Radiology, Michigan Medicine, Ann Arbor, MI, USA
Krishna Shanbhogue Department of Radiology, New York University, New York, NY, USA
Nehal A Parikh Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Jonathan R Dillman Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Lili He Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA. Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA. Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.

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Zarenia M, Zhang Y, Sarosiek C, Conlin R, Amjad A, Paulson E. Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours. Phys Med Biol 2024;69:10.1088/1361-6560/ad87a6. [PMID: 39413822 PMCID: PMC11551967 DOI: 10.1088/1361-6560/ad87a6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 10/16/2024] [Indexed: 10/18/2024]

Abstract

Objective.Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach.The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5 T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results.The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/126) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance.We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.

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El Homsi M, Bane O, Fauveau V, Hectors S, Vietti Violi N, Sylla P, Ko HB, Cuevas J, Carbonell G, Nehlsen A, Vanguri R, Viswanath S, Jambawalikar S, Shaish H, Taouli B. Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics. Abdom Radiol (NY) 2024;49:791-800. [PMID: 38150143 DOI: 10.1007/s00261-023-04128-0] [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: 07/06/2023] [Revised: 11/06/2023] [Accepted: 11/12/2023] [Indexed: 12/28/2023]

Abstract

PURPOSE

To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT).

METHODS

This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set.

RESULTS

Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%].

CONCLUSION

Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

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Affiliation(s)

Maria El Homsi Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA. Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York av, New York, USA.
Octavia Bane BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Valentin Fauveau BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Stefanie Hectors BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Naik Vietti Violi BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
Patricia Sylla Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Huai-Bin Ko Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Pathology, Columbia University Medical Center, New York, NY, USA
Jordan Cuevas BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Guillermo Carbonell BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain
Anthony Nehlsen Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Rami Vanguri Department of Epidemiology & Biostatistics, Columbia University Medical Center, New York, NY, USA
Satish Viswanath Department of Radiology, Case Western University, Cleveland, OH, USA
Sachin Jambawalikar Department of Radiology, Columbia University Medical Center, New York, NY, USA
Hiram Shaish Department of Radiology, Columbia University Medical Center, New York, NY, USA
Bachir Taouli Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

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Azuri I, Wattad A, Peri-Hanania K, Kashti T, Rosen R, Caspi Y, Istaiti M, Wattad M, Applbaum Y, Zimran A, Revel-Vilk S, C. Eldar Y. A Deep-Learning Approach to Spleen Volume Estimation in Patients with Gaucher Disease. J Clin Med 2023;12:5361. [PMID: 37629403 PMCID: PMC10455264 DOI: 10.3390/jcm12165361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open

Amjad A, Xu J, Thill D, Zhang Y, Ding J, Paulson E, Hall W, Erickson BA, Li XA. Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs. Front Oncol 2023;13:1209558. [PMID: 37483486 PMCID: PMC10358771 DOI: 10.3389/fonc.2023.1209558] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023] Open

Abstract

Introduction

Multi-sequence multi-parameter MRIs are often used to define targets and/or organs at risk (OAR) in radiation therapy (RT) planning. Deep learning has so far focused on developing auto-segmentation models based on a single MRI sequence. The purpose of this work is to develop a multi-sequence deep learning based auto-segmentation (mS-DLAS) based on multi-sequence abdominal MRIs.

Materials and methods

Using a previously developed 3DResUnet network, a mS-DLAS model using 4 T1 and T2 weighted MRI acquired during routine RT simulation for 71 cases with abdominal tumors was trained and tested. Strategies including data pre-processing, Z-normalization approach, and data augmentation were employed. Additional 2 sequence specific T1 weighted (T1-M) and T2 weighted (T2-M) models were trained to evaluate performance of sequence-specific DLAS. Performance of all models was quantitatively evaluated using 6 surface and volumetric accuracy metrics.

Results

The developed DLAS models were able to generate reasonable contours of 12 upper abdomen organs within 21 seconds for each testing case. The 3D average values of dice similarity coefficient (DSC), mean distance to agreement (MDA mm), 95 percentile Hausdorff distance (HD95% mm), percent volume difference (PVD), surface DSC (sDSC), and relative added path length (rAPL mm/cc) over all organs were 0.87, 1.79, 7.43, -8.95, 0.82, and 12.25, respectively, for mS-DLAS model. Collectively, 71% of the auto-segmented contours by the three models had relatively high quality. Additionally, the obtained mS-DLAS successfully segmented 9 out of 16 MRI sequences that were not used in the model training.

Conclusion

We have developed an MRI-based mS-DLAS model for auto-segmenting of upper abdominal organs on MRI. Multi-sequence segmentation is desirable in routine clinical practice of RT for accurate organ and target delineation, particularly for abdominal tumors. Our work will act as a stepping stone for acquiring fast and accurate segmentation on multi-contrast MRI and make way for MR only guided radiation therapy.

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Berbís MA, Paulano Godino F, Royuela del Val J, Alcalá Mata L, Luna A. Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver. World J Gastroenterol 2023;29:1427-1445. [PMID: 36998424 PMCID: PMC10044858 DOI: 10.3748/wjg.v29.i9.1427] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 01/13/2023] [Accepted: 02/27/2023] [Indexed: 03/07/2023] Open

Abstract

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

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Huang SY, Hsu WL, Hsu RJ, Liu DW. Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey. Diagnostics (Basel) 2022;12:diagnostics12112765. [PMID: 36428824 PMCID: PMC9689961 DOI: 10.3390/diagnostics12112765] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/19/2022] [Accepted: 11/04/2022] [Indexed: 11/16/2022] Open

Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022;12:973999. [PMID: 35982967 PMCID: PMC9380440 DOI: 10.3389/fonc.2022.973999] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open

Altini N, Prencipe B, Cascarano GD, Brunetti A, Brunetti G, Triggiani V, Carnimeo L, Marino F, Guerriero A, Villani L, Scardapane A, Bevilacqua V. Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.08.157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]

Roger R, Hilmes MA, Williams JM, Moore DJ, Powers AC, Craddock RC, Virostko J. Deep learning-based pancreas volume assessment in individuals with type 1 diabetes. BMC Med Imaging 2022;22:5. [PMID: 34986790 PMCID: PMC8734282 DOI: 10.1186/s12880-021-00729-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/10/2021] [Indexed: 01/11/2023] Open

Meddeb A, Kossen T, Bressem KK, Hamm B, Nagel SN. Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen. Tomography 2021;7:950-960. [PMID: 34941650 PMCID: PMC8704906 DOI: 10.3390/tomography7040078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open

Feng DY, Ren Y, Zhou M, Zou XL, Wu WB, Yang HL, Zhou YQ, Zhang TT. Deep Learning-Based Available and Common Clinical-Related Feature Variables Robustly Predict Survival in Community-Acquired Pneumonia. Risk Manag Healthc Policy 2021;14:3701-3709. [PMID: 34512057 PMCID: PMC8427836 DOI: 10.2147/rmhp.s317735] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/14/2021] [Indexed: 01/16/2023] Open

Abstract

Background

Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality worldwide. Although there are many predictors of death for CAP, there are still some limitations. This study aimed to build a simple and accurate model based on available and common clinical-related feature variables for predicting CAP mortality by adopting machine learning techniques.

Methods

This was a single-center retrospective study. The data used in this study were collected from all patients (≥18 years) with CAP admitted to research hospitals between January 2012 and April 2020. Each patient had 62 clinical-related features, including clinical diagnostic and treatment features. Patients were divided into two endpoints, and by using Tensorflow2.4.1 as the modeling framework, a three-layer fully connected neural network (FCNN) was built as a base model for classification. For a comprehensive comparison, seven classical machine learning methods and their integrated stacking patterns were introduced to model and compare the same training and test data.

Results

A total of 3997 patients with CAP were included; 205 (5.12%) died in the hospital. After performing deep learning methods, this study established an ensemble FCNN model based on 12 FCNNs. By comparing with seven classical machine learning methods, the area under the curve of the ensemble FCNN was 0.975 when using deep learning algorithms to classify poor from good prognosis based on available and common clinical-related feature variables. The predicted outcome was poor prognosis if the ControlNet's poor prognosis score was greater than the cutoff value of 0.50. To confirm the scientificity of the ensemble FCNN model, this study analyzed the weight of random forest features and found that mainstream prognostic features still held weight, although the model is perfect after integrating other factors considered less important by previous studies.

Conclusion

This study used deep learning algorithms to classify prognosis based on available and common clinical-related feature variables in patients with CAP with high accuracy and good generalizability. Every clinical-related feature is important to the model.

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Hamwood J, Schmutz B, Collins MJ, Allenby MC, Alonso-Caneiro D. A deep learning method for automatic segmentation of the bony orbit in MRI and CT images. Sci Rep 2021;11:13693. [PMID: 34211081 PMCID: PMC8249400 DOI: 10.1038/s41598-021-93227-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 06/15/2021] [Indexed: 12/23/2022] Open

Barat M, Chassagnon G, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Soyer P. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol 2021;39:514-523. [PMID: 33550513 DOI: 10.1007/s11604-021-01098-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 12/11/2022]

Humpire-Mamani GE, Bukala J, Scholten ET, Prokop M, van Ginneken B, Jacobs C. Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning. Radiol Artif Intell 2021;2:e190102. [PMID: 33937830 DOI: 10.1148/ryai.2020190102] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 04/26/2020] [Accepted: 05/01/2020] [Indexed: 12/15/2022]

Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising. World J Gastroenterol 2021;27:1283-1295. [PMID: 33833482 PMCID: PMC8015296 DOI: 10.3748/wjg.v27.i13.1283] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/22/2021] [Accepted: 03/13/2021] [Indexed: 02/06/2023] Open

Furtado P. Testing Segmentation Popular Loss and Variations in Three Multiclass Medical Imaging Problems. J Imaging 2021;7:16. [PMID: 34460615 PMCID: PMC8321275 DOI: 10.3390/jimaging7020016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/16/2021] [Accepted: 01/22/2021] [Indexed: 12/15/2022] Open

Anderson BM, Lin EY, Cardenas CE, Gress DA, Erwin WD, Odisio BC, Koay EJ, Brock KK. Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks. Adv Radiat Oncol 2021;6:100464. [PMID: 33490720 PMCID: PMC7807136 DOI: 10.1016/j.adro.2020.04.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 04/14/2020] [Accepted: 04/25/2020] [Indexed: 11/07/2022] Open

Abstract

PURPOSE

The deformable nature of the liver can make focal treatment challenging and is not adequately addressed with simple rigid registration techniques. More advanced registration techniques can take deformations into account (eg, biomechanical modeling) but require segmentations of the whole liver for each scan, which is a time-intensive process. We hypothesize that fully convolutional networks can be used to rapidly and accurately autosegment the liver, removing the temporal bottleneck for biomechanical modeling.

METHODS AND MATERIALS

Manual liver segmentations on computed tomography scans from 183 patients treated at our institution and 30 scans from the Medical Image Computing & Computer Assisted Intervention challenges were collected for this study. Three architectures were investigated for rapid automated segmentation of the liver (VGG-16, DeepLabv3 +, and a 3-dimensional UNet). Fifty-six cases were set aside as a final test set for quantitative model evaluation. Accuracy of the autosegmentations was assessed using Dice similarity coefficient and mean surface distance. Qualitative evaluation was also performed by 3 radiation oncologists on 50 independent cases with previously clinically treated liver contours.

RESULTS

The mean (minimum-maximum) mean surface distance for the test groups with the final model, DeepLabv3 +, were as follows: μContrast(N = 17): 0.99 mm (0.47-2.2), μNon_Contrast(N = 19)l: 1.12 mm (0.41-2.87), and μMiccai(N = 30)t: 1.48 mm (0.82-3.96). The qualitative evaluation showed that 30 of 50 autosegmentations (60%) were preferred to manual contours (majority voting) in a blinded comparison, and 48 of 50 autosegmentations (96%) were deemed clinically acceptable by at least 1 reviewing physician.

CONCLUSIONS

The autosegmentations were preferred compared with manually defined contours in the majority of cases. The ability to rapidly segment the liver with high accuracy achieved in this investigation has the potential to enable the efficient integration of biomechanical model-based registration into a clinical workflow.

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Convolutional neural network-based automatic liver delineation on contrast-enhanced and non-contrast-enhanced CT images for radiotherapy planning. Rep Pract Oncol Radiother 2020;25:981-986. [DOI: 10.1016/j.rpor.2020.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 08/23/2020] [Accepted: 09/21/2020] [Indexed: 11/21/2022] Open

Takagi S, Sakuma S, Morita I, Sugimoto E, Yamaguchi Y, Higuchi N, Inamoto K, Ariji Y, Ariji E, Murakami H. Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment. J Clin Med 2020;9:E3475. [PMID: 33126595 PMCID: PMC7693464 DOI: 10.3390/jcm9113475] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/14/2020] [Accepted: 10/27/2020] [Indexed: 12/05/2022] Open

Chen Y, Ruan D, Xiao J, Wang L, Sun B, Saouaf R, Yang W, Li D, Fan Z. Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks. Med Phys 2020;47:4971-4982. [PMID: 32748401 DOI: 10.1002/mp.14429] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 07/12/2020] [Accepted: 07/17/2020] [Indexed: 02/06/2023] Open

Abstract

PURPOSE

Segmentation of multiple organs-at-risk (OARs) is essential for magnetic resonance (MR)-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency.

METHODS

We developed Automated deep Learning-based abdominal multiorgan segmentation (ALAMO) technique based on two-dimensional U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance.

RESULTS

The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within 1 min for a three-dimensional volume of 320 × 288 × 180. Overall, the ALAMO model matched the state-of-the-art techniques in performance.

CONCLUSION

The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.

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Sanders JW, Fletcher JR, Frank SJ, Liu HL, Johnson JM, Zhou Z, Chen HSM, Venkatesan AM, Kudchadker RJ, Pagel MD, Ma J. Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging. SOFTWAREX 2019;10:100347. [PMID: 34113706 PMCID: PMC8188855 DOI: 10.1016/j.softx.2019.100347] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]

Affiliation(s)

Jeremiah W. Sanders Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
Justin R. Fletcher Odyssey Systems Consulting, LLC, 550 Lipoa Parkway, Kihei, Maui, HI, United States of America
Steven J. Frank Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1422, Houston, TX 77030, United States of America
Ho-Ling Liu Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America
Jason M. Johnson Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1473, Houston, TX 77030, United States of America
Zijian Zhou Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
Henry Szu-Meng Chen Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America
Aradhana M. Venkatesan Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1473, Houston, TX 77030, United States of America
Rajat J. Kudchadker Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, TX 77030, United States of America
Mark D. Pagel Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1907, Houston, TX 77030, United States of America
Jingfei Ma Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, 1515 Holcombe Blvd., Unit 1472, TX 77030, United States of America

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Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol 2019;92:20190001. [PMID: 31112393 PMCID: PMC6724618 DOI: 10.1259/bjr.20190001] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open

Virostko J, Williams J, Hilmes M, Bowman C, Wright JJ, Du L, Kang H, Russell WE, Powers AC, Moore DJ. Pancreas Volume Declines During the First Year After Diagnosis of Type 1 Diabetes and Exhibits Altered Diffusion at Disease Onset. Diabetes Care 2019;42:248-257. [PMID: 30552135 PMCID: PMC6341292 DOI: 10.2337/dc18-1507] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 11/15/2018] [Indexed: 02/03/2023]

Abstract

OBJECTIVE

This study investigated the temporal dynamics of pancreas volume and microstructure in children and adolescents with recent-onset type 1 diabetes (T1D) and individuals without diabetes, including a subset expressing autoantibodies associated with the early stages of T1D.

RESEARCH DESIGN AND METHODS

MRI was performed in individuals with recent-onset stage 3 T1D (n = 51; median age 13 years) within 100 days after diagnosis (mean 67 days), 6 months, and 1 year postdiagnosis. Longitudinal MRI measurements were also made in similarly aged control participants (n = 57) and in autoantibody-positive individuals without diabetes (n = 20). The MRI protocol consisted of anatomical imaging to determine pancreas volume and quantitative MRI protocols interrogating tissue microstructure and composition.

RESULTS

Within 100 days of diabetes onset, individuals with T1D had a smaller pancreas (median volume 28.6 mL) than control participants (median volume 48.4 mL; P < 0.001), including when normalized by individual weight (P < 0.001). Longitudinal measurements of pancreas volume increased in control participants over the year, consistent with adolescent growth, but pancreas volume declined over the first year after T1D diagnosis (P < 0.001). In multiple autoantibody-positive individuals, the pancreas volume was significantly larger than that of the T1D cohort (P = 0.017) but smaller than that of the control cohort (P = 0.04). Diffusion-weighted MRI showed that individuals with recent-onset T1D had a higher apparent diffusion coefficient (P = 0.012), suggesting a loss of cellular structural integrity, with heterogeneous pancreatic distribution.

CONCLUSIONS

These results indicate that pancreas volume is decreased in stages 1, 2, and 3 of T1D and decreases during the first year after diabetes onset and that this loss of pancreatic volume is accompanied by microstructural changes.

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