Deep Learning Based Rectal Tumor Localization and Segmentation on Multi-parametric MRI

Motivation: The study is motivated by need to improve rectal cancer treatment planning through deep-learning-based analysis of multiparametric MRI, replacing inconsistent and labor-intensive manual tumor delineation. Goal(s): The study aims to develop a deep-learning algorithm for automated rectal cancer segmentation in MRI images to improve treatment response predictions. Approach: A two-tiered U-net architecture with attention gates, optimized through cross-validation, was applied to multi-parametric MRI data from 198 patients. Results: This approach outperformed existing models, with the highest accuracy achieved by combining different MRI sequences. The results indicate that incorporating functional MRI data with anatomical imaging significantly enhances tumor delineation, potentially informing personalized treatment strategies. Impact: This deep-learning model significantly improves rectal cancer MRI segmentation, offering a path to more accurate and personalized treatment strategies, potentially leading to better patient outcomes and streamlined workflows in oncological imaging and radiation therapy planning.