A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
Issued Date
2022-01-01
Resource Type
eISSN
23765992
Scopus ID
2-s2.0-85134495805
Journal Title
PeerJ Computer Science
Volume
8
Rights Holder(s)
SCOPUS
Bibliographic Citation
PeerJ Computer Science Vol.8 (2022)
Suggested Citation
Siriapisith T., Kusakunniran W., Haddawy P. A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm. PeerJ Computer Science Vol.8 (2022). doi:10.7717/peerj-cs.1033 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84386
Title
A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre and post-operative abdominal aortic aneurysm
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
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