Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning

This study is focused on developing an automated algorithm for the detection and segmentation of Abdominal Aortic Aneurysm (AAA) region in CT Angiography images. The outcome of this research will offer great assistance for radiologists to detect the AAA region and estimate its volume in CT datasets...

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Main Author:	Ho, Aik Hong
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/81027/1/HoAikHongMFKE2015.pdf
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id	my-utm-ep.81027
record_format	uketd_dc
spelling	my-utm-ep.810272019-07-24T03:06:05Z Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning 2015-06 Ho, Aik Hong TK Electrical engineering. Electronics Nuclear engineering This study is focused on developing an automated algorithm for the detection and segmentation of Abdominal Aortic Aneurysm (AAA) region in CT Angiography images. The outcome of this research will offer great assistance for radiologists to detect the AAA region and estimate its volume in CT datasets efficiently. In addition, suitable treatment strategies can also be suggested based on the classification of the AAA severity and measurement of the aorta diameter. This research takes the initiative by exploring and applying deep learning architecture in the study of AAA detection and segmentation, which has never been done by other researchers before in AAA problems. The findings from this study will also act as a reference for other similar future works. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing method with advantage over the existing method that the proposed method is fully automatic and added with auto detection and classification features. The limitation of the trained DBN in AAA detection accuracy can be improved by incorporating and adjusting the detection probability threshold and shape constraints. In future, the DBN can be further enhanced by adding and training it with more data which covers a wider variety of features, as well as extending its capability to perform detailed segmentation on AAA region. 2015-06 Thesis http://eprints.utm.my/id/eprint/81027/ http://eprints.utm.my/id/eprint/81027/1/HoAikHongMFKE2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:121241 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ho, Aik Hong Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
description	This study is focused on developing an automated algorithm for the detection and segmentation of Abdominal Aortic Aneurysm (AAA) region in CT Angiography images. The outcome of this research will offer great assistance for radiologists to detect the AAA region and estimate its volume in CT datasets efficiently. In addition, suitable treatment strategies can also be suggested based on the classification of the AAA severity and measurement of the aorta diameter. This research takes the initiative by exploring and applying deep learning architecture in the study of AAA detection and segmentation, which has never been done by other researchers before in AAA problems. The findings from this study will also act as a reference for other similar future works. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing method with advantage over the existing method that the proposed method is fully automatic and added with auto detection and classification features. The limitation of the trained DBN in AAA detection accuracy can be improved by incorporating and adjusting the detection probability threshold and shape constraints. In future, the DBN can be further enhanced by adding and training it with more data which covers a wider variety of features, as well as extending its capability to perform detailed segmentation on AAA region.
format	Thesis
qualification_level	Master's degree
author	Ho, Aik Hong
author_facet	Ho, Aik Hong
author_sort	Ho, Aik Hong
title	Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
title_short	Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
title_full	Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
title_fullStr	Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
title_full_unstemmed	Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
title_sort	automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	http://eprints.utm.my/id/eprint/81027/1/HoAikHongMFKE2015.pdf
_version_	1747818295132684288

Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning

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