DEEP LEARNING-BASED SEGMENTATION FOR PRECISION RADIATION THERAPY IN BREAST CANCER

Abstract

Breast cancer is a major health burden, and clinicians need accurate tumor segmentation to deliver radiation therapy precisely and efficiently. This thesis bench- marks 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 imag- ing). 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 visualiza- tions 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.