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Machine Learning for Abdominal Aortic Aneurysm Characterization from Standard-Of-Care Computed Tomography Angiography Images

Salvi, Anish (2023) Machine Learning for Abdominal Aortic Aneurysm Characterization from Standard-Of-Care Computed Tomography Angiography Images. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abdominal aortic aneurysms (AAAs) are dilations in the descending aorta which can result in internal bleeding when ruptured, leading to hospitalization or death. AAAs are commonly asymptomatic and discovered by happenstance during imaging tests, including computed tomography (CT) and its blood vessel enhancing counterpart computed tomography angiography (CTA). However, a past evaluation indicates that radiologists correctly identified and referred to monitoring only 32%, or 43 of 133 AAAs, from 3292 CTs. AAAs with larger diameters (> 5 cm) are recommended for elective repair; however, < 5 cm AAAs may have a rupture rate as high as 23%. Utilizing diameter as a one size fits all approach fails to consider intraluminal thrombus (ILT) and calcifications, clinically relevant attributes associated with elevated rupture risk. While a prior study indicated that type I and III endoleaks, linked with incorrect graft positioning during elective repair, had an incidence of only 6.4%, these complications require urgent medical attention. Surgical planning may benefit from greater understanding of AAA geometry. There remains a critical need for the automated discovery, visualization, and elective repair indication of AAAs. Having explored the novel field at the intersection of state-of-the-art machine learning and standard-of-care medical imaging, we develop deep learning models to aid the detection, segmentation, and classification of AAAs based on pre-operative CTA characteristics while observing the frame-of-reference. We describe computational methods which include bounding box localization as a precursor to high-resolution segmentation, patch-based segmentation of medical image sub-volumes, image transformers that identify AAA severity, and a vision transformer that provides heatmaps indicative of AAA severity prediction. We find that 1) our memory-efficient bounding box method outperforms conventional neural network based AAA lumen segmentation, 2) patch-based AAA wall segmentation has improved performance as compared to our memory efficient computational pipeline for asymptomatic cases, 3) image transformers approach and even beat the accuracy achieved by rudimentary classifiers (i.e., differentiating between asymptomatic v. symptomatic AAAs) when leveraging embeddings derived from class specific segmentation models, and 4) vision transformers not only predict AAA severity accurately, but localize the disease by its anatomical basis. In sum, we make key contributions to scientific literature concerning medical imaging and machine learning through our computational methods of AAA interpretation.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Salvi, Anishans314@pitt.eduans314
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMenon, Prahlad
Committee MemberFinol,
Committee MemberShroff,
Date: 19 January 2023
Date Type: Publication
Defense Date: 7 November 2022
Approval Date: 19 January 2023
Submission Date: 7 November 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 91
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Bioengineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: abdominal aortic aneurysms, deep learning, convolutional neural networks, vision transformers, image transformers
Date Deposited: 19 Jan 2023 19:26
Last Modified: 19 Jan 2023 19:26

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  • Machine Learning for Abdominal Aortic Aneurysm Characterization from Standard-Of-Care Computed Tomography Angiography Images. (deposited 19 Jan 2023 19:26) [Currently Displayed]


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