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- Automated Tumor Segmentation in PET/CT Scans of Lymphoma...
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Automated Tumor Segmentation in PET/CT Scans of Lymphoma Patients Yin, Leyi (Bellinda)
Abstract
Lymphoma is a heterogeneous disease that can manifest in over 500 lymph nodes throughout the body in addition to several lymphatic organs like the bone marrow and spleen. Assessment of disease burden, staging and prognosis is typically done by visually assessing full-body PET/CT scans. With hundreds of tumors possible, manual detection and delineation of each individual lesion is extremely time-consuming, prone to inter- and intra-observer variability, and only results in a qualitative analysis, leaving out vital quantitative metrics. Although not routinely performed in clinics, full segmentation of patient tumors can provide valuable quantitative metrics (such as metabolic tumor volume) that aid in predicting outcome and developing individualized treatment. A fully automatic pipeline for PET/CT lymphoma images for detection and delineation of the lymphoma lesions is therefore, of great importance. In recent years, with the advancement of Artificial Intelligence (AI), numerous segmentation schemes based on supervised deep learning models have been proposed that require a large number of detailed delineated cases. While detection techniques only need roughly annotated data, they can not be used to extract exact tumor boundaries. For this thesis, an automatic segmentation pipeline based on conventional segmentation methods will be implemented in MATLAB with a focus on optimizing delineation algorithms. These results will then be used to refine the output of AI-based object detection techniques, such as YOLO network. Using this routine, the work of this thesis intends to present an accurate technique for individual lesion segmentation and enhance the diagnostic workflow.
Item Metadata
Title |
Automated Tumor Segmentation in PET/CT Scans of Lymphoma Patients
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Creator | |
Date Issued |
2021-04
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Description |
Lymphoma is a heterogeneous disease that can manifest in over 500 lymph nodes
throughout the body in addition to several lymphatic organs like the bone marrow and spleen. Assessment of disease burden, staging and prognosis is typically
done by visually assessing full-body PET/CT scans. With hundreds of tumors
possible, manual detection and delineation of each individual lesion is extremely
time-consuming, prone to inter- and intra-observer variability, and only results in
a qualitative analysis, leaving out vital quantitative metrics. Although not routinely performed in clinics, full segmentation of patient tumors can provide valuable quantitative metrics (such as metabolic tumor volume) that aid in predicting
outcome and developing individualized treatment. A fully automatic pipeline for
PET/CT lymphoma images for detection and delineation of the lymphoma lesions
is therefore, of great importance. In recent years, with the advancement of Artificial
Intelligence (AI), numerous segmentation schemes based on supervised deep learning models have been proposed that require a large number of detailed delineated
cases. While detection techniques only need roughly annotated data, they can not
be used to extract exact tumor boundaries. For this thesis, an automatic segmentation pipeline based on conventional segmentation methods will be implemented
in MATLAB with a focus on optimizing delineation algorithms. These results will
then be used to refine the output of AI-based object detection techniques, such as
YOLO network. Using this routine, the work of this thesis intends to present an
accurate technique for individual lesion segmentation and enhance the diagnostic
workflow.
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Genre | |
Type | |
Language |
eng
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Series | |
Date Available |
2021-05-13
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0397465
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Undergraduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International