Observational Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Jan 28, 2020; 12(1): 1-9
Published online Jan 28, 2020. doi: 10.4329/wjr.v12.i1.1
Segmentation of carotid arterial walls using neural networks
Daniel D Samber, Sarayu Ramachandran, Anoop Sahota, Sonum Naidu, Alison Pruzan, Zahi A Fayad, Venkatesh Mani
Daniel D Samber, Sarayu Ramachandran, Anoop Sahota, Sonum Naidu, Alison Pruzan, Zahi A Fayad, Venkatesh Mani, Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
Author contributions: Samber DD programmed the analysis software and wrote the draft of the manuscript; Ramachandran S assembled and pre-processed imaging data; Naidu S, Sahota A, and Pruzan A performed the image analysis; Fayad ZA and Mani V oversaw the analysis; all authors critically reviewed the manuscript.
Supported by American Heart Association Grant in Aid Founders Affiliate No. 17GRNT33420119 (Mani V), NIH NHLBI 2R01HL070121 (Fayad ZA) and NIH NHLBI 1R01HL135878 (Fayad ZA).
Institutional review board statement: The study was approved by the Institutional Review Board of the Icahn School of Medicine at Mount Sinai.
Informed consent statement: Waiver of institutional review board (IRB) approval was obtained from the IRB as only deidentified data was used in this study. The images analyzed for this study were anonymized and devoid of any Protected Health Information.
Conflict-of-interest statement: No conflicts to disclose.
Data sharing statement: Once published and after appropriate safeguard to ensure that the data is devoid of any identifiers, the data used for the analysis for this study will be shared on the Mount Sinai data sharing portal according to Institutional guidelines.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Daniel D Samber, BSc, Research Scientist, Translational Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY 10029, United States. daniel.samber@mssm.edu
Received: July 19, 2019
Peer-review started: July 21, 2019
First decision: September 21, 2019
Revised: October 11, 2019
Accepted: November 20, 2019
Article in press: November 20, 2019
Published online: January 28, 2020
Processing time: 152 Days and 20.7 Hours
ARTICLE HIGHLIGHTS
Research background

Segmentation of arterial vessels is an important step is the assessment of vascular disease. For many years, the accepted method of producing segmentations was through manual approach performed by expert researchers. We apply the technique of convolutional neural networks (CNNs) to the task of segmentation of carotid arteries and compare the results to the manual method.

Research motivation

The accepted standard of manual segmentation by expert researchers is an onerous and time-consuming task that is inherently subjective. Consequently, constructing an algorithm from such an opaque process is problematic. Creation and adoption of a reliable segmentation algorithm could lead to significant savings through automation.

Research objectives

The objective in this study was to examine the feasibility of applying CNNs to the task of segmenting carotid arteries of subjects with vascular disease.

Research methods

Subsets of magnetic resonance images of the carotid arteries of 189 subjects with atherosclerotic disease were used to train and subsequently validate the CNN. Image segmentations used to train the CNN were produced by an expert reader who manually segmented individual images of the carotid wall using conventional means resulting in a dataset of 4422 segmented images. In preparation for automated segmentation, the original dataset was divided into 3 groups: A “training dataset” (3581 images), a “validation dataset” (398 images), and a “test dataset” (443 images). These datasets were used to train two separate segmentation CNNs (one for carotid lumen and the other for carotid wall). After training, images from the test dataset were processed to produce segmentations as binary images.

Research results

Overall quantitative assessment between manual and automated segmentations was determined by computing the DICE coefficient for each pair of segmented images in the test dataset. The average DICE coefficient between automated and manual segmentations was 0.87 for the carotid vessel wall and 0.96 for the carotid lumen. Intra-class correlation coefficients (ICC) as well as Pearson correlation values were computed for vessel area metrics as determined for the expert reader and the CNN to assess the agreement of measurements. Excellent agreement was observed in the segmentation of lumen area (Pearson correlation = 0.98, ICC = 0.98) as well as in the segmentation of vessel wall area (Pearson correlation = 0.88, ICC = 0.86). Additionally, Bland-Altman plots of these measurements for the CNN and reader indicate good agreement.

Research conclusions

In this study, we have demonstrated the effectiveness of CNN technology in its application to the task of delineating carotid vessel walls thereby facilitating the detection of potential pathology.

Research perspectives

Although the technique produces reasonable results that are on par with expert human assessments, in our application it requires human supervision and monitoring to ensure consistent results. We intend to deploy this algorithm as part of a software platform to lessen researchers workload to more quickly obtain reliable results.