A machine learning approach to computed tomography organ segmentation for quality assurance
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Borden, Bailey
issued: 2024-12-05T20:06:22Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Purpose: Artificial intelligence (AI) autocontouring tools are becoming readily available, and can also be created using published deep neural network (DNN) frameworks. With the labour demand required to peer review cases, contouring peer review may not occur before the beginning of treatment. This work investigates the use of AI for autocontouring to aid within the breast radiotherapy contouring peer review process. The use of AI within contour peer review may aid with labour demands and allow patient cases to be reviewed earlier in treatment. Methods: Using open-source deep learning libraries, DNNs were trained to segment the heart, the left lung, and the right lung. The model architectures explored in this work are U-Net and LinkNet. The trained architectures were compared to the commercial algorithm Limbus AI. The trained U-Net model architecture was selected to demonstrate uncertainty quantification using conformal prediction, while Limbus AI was used to show an approach to model uncertainty using test time augmentation (TTA). Results: For multiclass segmentation explored, U-net and Linknet achieved a mean intersection over union (IoU) of 0.924 and 0.910 respectively. When the trained algorithms were compared to the commercial algorithm, U-Net and LinkNet achieved mean IoUs of 0.914 and 0.911, while Limbus AI achieved a mean IoU of 0.883. Using the conformal prediction methodology on U-Net, uncertain pixels within the model's predictions were found to be on the boundaries of structures. When testing the uncertainty of the Limbus AI using TTA, a standard error of 4.95% was observed on the heart predictions, 3.54% on the left lung predictions, and 3.35% on the right lung predictions. Conclusion: Based on the literature, evaluating contours using both area and distance based metrics is recommended for peer review quality assurance. Testing of a selected model using a sample of data representing the potential patient demographic is recommended. Additionally, testin…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89784
note: A Machine Learning Approach toComputed Tomography OrganSegmentation for Quality AssurancebyBailey BordenB.Sc. Physics (Honours), The University of Northern British Columbia, 2022A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Medical Physics)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)November 2024© Bailey Borden 2024The following individuals certify that they have read, and recommend to the Col-lege of Graduate Studies for acceptance, the thesis entitled:A Machine Learning Approach to Computed Tomography Organ Segmentationfor Quality Assurancesubmitted by Bailey Borden in partial fulfillment of the requirements for the de-gree of Master of Science in Medical PhysicsExamining Committee:Dr. Rasika Rajapakshe, Irving K Barber Faculty of ScienceSupervisorDr. Rebecca Feldman, Irving K Barber Faculty of ScienceCo-supervisorDr. Patricia Lasserre, Irving K Barber Faculty of ScienceSupervisory Committee MemberDr. Sarah Lucas, Department of SurgerySupervisory Committee MemberDr. Jonathan Holzman, School of EngineeringUniversity ExamineriiAbstractPurpose: Artificial intelligence (AI) autocontouring tools are becoming readilyavailable, and can also be created using published deep neural network (DNN)frameworks. With the labour demand required to peer review cases, contouringpeer review may not occur before the beginning of treatment. This work investi-gates the use of AI for autocontouring to aid within the breast radiotherapy con-touring peer review process. The use of AI within contour peer review may aidwith labour demands and allow patient cases to be reviewed earlier in treatment.Methods: Using open-source deep learning libraries, DNNs were trained to seg-ment the heart, the left lung, and the right lung. The model architectures exploredin this work are U-Net and LinkNet. The trained architectures were comparedto the commercial algorithm Limbus AI. The trained U-Net model architecturewas sele…
has type: Thesis/Dissertation
date issued: 2025-02
is shown at: 10.14288/1.0447409
language: eng
degree discipline: Medical Physics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Rajapakshe, Rasika
contributor: Feldman, Rebecca
type: Text
identifier URI: http://hdl.handle.net/2429/89784