Automated detection and segmentation of metastatic prostate cancer lesions in PET/CT images via deep neural networks towards accurate and routine quantification of clinical metrics
department or school: Applied Science, Faculty of
department or school: Biomedical Engineering, School of
data provider: DSpace
degree campus: UBCV
creator: Dzikunu, Obed Korshie
issued: 2025-01-06T20:00:14Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Detection and segmentation of lesions in metastatic prostate cancer patients via multimodal positron emission tomography (PET)/computed tomography (CT) imaging can have significant value in the clinical management spectrum, including staging, restaging, diagnosis, treatment planning, and prognosis. Conventional manual segmentation methods, such as tumor contouring, are labor intensive and time-consuming, driving interest in automated solutions using artificial intelligence, specifically deep neural networks. However, automated segmentation faces challenges due to tumor variability in size, shape, uptake intensity, and anatomical location, as well as significant class imbalance between lesion and background voxels, and variability in voxel classification difficulty. Loss function design is central to addressing these challenges. While Dice loss effectively handles class imbalance, it struggles with voxel-level classification difficulty. Nevertheless, distribution-based loss functions like cross-entropy and focal loss address classification difficulty but compromise Dice loss's balance of false positives and false negatives. Combining these loss functions into compound forms improves robustness but often trades off specificity for sensitivity, limiting clinical utility. To address these issues, we propose a novel loss function, L1-weighted Dice Focal Loss (L1DFL), which uses L1 norms of predicted probabilities and ground truth labels to prioritize underrepresented classes and hard-to-classify voxels. Performance was evaluated across multiple lesion scenarios, including images with single and multiple lesions, and varying molecular volumes. We also evaluated its robustness across various levels of spatial distribution of lesions, beyond the primary prostatic region. Subsequently, the analysis evaluated the loss function's clinical utility and its ability to reproduce clinical metrics. Experimental results demonstrate that L1DFL outperforms Dice Focal Loss and Dice Loss…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/90059
note: Automated Detection andSegmentation of Metastatic ProstateCancer Lesions in PET/CT Images viaDeep Neural Networks TowardsAccurate and Routine Quantificationof Clinical MetricsbyObed Korshie DzikunuB.Sc., University of Ghana, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Biomedical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)December 2024© Obed Korshie Dzikunu 2024The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Automated Detection and Segmentation of Metastatic Prostate Cancer Lesionsin PET/CT Images via Deep Neural Networks Towards Accurate and RoutineQuantification of Clinical Metricssubmitted by Obed Korshie Dzikunu in partial fulfillment of the require-ments for the degree of Master of Applied Sciencein Biomedical EngineeringExamining Committee:Arman Rahmim, Professor, Departments of Radiology and Physics & Astronomy,UBCSupervisorXiaoxiao Li, Assistant Professor, Department of Electrical and ComputerEngineering, UBCSupervisory Committee MemberCarlos Uribe, Clinical Assistant Professor, Department of Radiology, UBCSupervisory Committee MemberiiAbstractDetection and segmentation of lesions in metastatic prostate cancer patientsvia multimodal positron emission tomography (PET)/computed tomogra-phy (CT) imaging can have significant value in the clinical management spec-trum, including staging, restaging, diagnosis, treatment planning, and prog-nosis. Conventional manual segmentation methods, such as tumor contour-ing, are labor intensive and time-consuming, driving interest in automatedsolutions using artificial intelligence, specifically deep neural networks. How-ever, automated segmentation faces challenges due to tumor variability insize, shape, uptake intensity, and anatomical location, as well as significantclass imbalance between lesion and …
has type: Thesis/Dissertation
date issued: 2025-05
is shown at: 10.14288/1.0447686
language: eng
degree discipline: Biomedical Engineering
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: Rahmim, Arman
type: Text
identifier URI: http://hdl.handle.net/2429/90059