Date of Award
Fall 11-28-2025
Document Type
Thesis
Publication Status
Version of Record
Submission Date
December 2025
Department
Physics
College Granting Degree
Charles E. Schmidt College of Science
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Wazir Muhammad
Abstract
Breast cancer is a major health burden, and clinicians need accurate tumor segmentation to deliver radiation therapy precisely and efficiently. This thesis benchmarks two three-dimensional (3D) deep learning architectures U-Net and SegResNet for automated segmentation of breast tumors on dynamic contrast-enhanced MRI. This work uses the MAMA-MIA benchmark, a (large-scale multicenter dataset for developing and evaluating artificial intelligence (AI) models for breast cancer imaging). MAMA-MIA consist of 1,506 breat cancer subjects. We applied a standardized Medical Open Network for AI (MONAI) preprocessing and training pipeline to build and evaluate deep-learning models for medical imaging. Models were assessed with the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), overall accuracy, and the 95th-percentile Hausdorff distance (HD95), alongside qualitative visualizations and Bland–Altman analyses. U-Net achieved DSC 0.7334, IoU 0.5791, accuracy 0.9984, HD95 33.13 mm, loss 0.0836, and 333.6 s/epoch over 60 epochs. SegResNet achieved DSC 0.7132, IoU 0.5542, accuracy 0.9981, HD95 37.58 mm, loss 0.0915, and 546.1 s/epoch over 60 epochs. Our results show that, U-Net achieved higher overlap and boundary metrics than SegResNet. These findings are preliminary and limited to tumor masks on this dataset; no external validation, user study, or clinical deployment was performed.
Recommended Citation
Alanazi, Hamdah, "DEEP LEARNING-BASED SEGMENTATION FOR PRECISION RADIATION THERAPY IN BREAST CANCER" (2025). Electronic Theses and Dissertations. 171.
https://digitalcommons.fau.edu/etd_general/171