A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentati...

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Main Author: Siriapisith T.
Other Authors: Mahidol University
Format: Article
Published: 2023
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/84386
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spelling th-mahidol.843862023-06-19T00:03:43Z A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm Siriapisith T. Mahidol University Computer Science Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSVUNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively 2023-06-18T17:03:43Z 2023-06-18T17:03:43Z 2022-01-01 Article PeerJ Computer Science Vol.8 (2022) 10.7717/peerj-cs.1033 23765992 2-s2.0-85134495805 https://repository.li.mahidol.ac.th/handle/123456789/84386 SCOPUS
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Computer Science
spellingShingle Computer Science
Siriapisith T.
A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
description Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSVUNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively
author2 Mahidol University
author_facet Mahidol University
Siriapisith T.
format Article
author Siriapisith T.
author_sort Siriapisith T.
title A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
title_short A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
title_full A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
title_fullStr A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
title_full_unstemmed A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
title_sort retrospective study of 3d deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
publishDate 2023
url https://repository.li.mahidol.ac.th/handle/123456789/84386
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