Organ-specific deep learning networks for Brachytherapy.

Nirmalya Gayen, Aditya Kumar, Bhuneshwar Netam, Nithin Shivashankar, Kirthi Koushik, and Vijay Natarajan.
BIOIMAGING, 2026, In Press.

Abstract

The accurate identification of anatomical structures within volumetric data derived from medical scanning devices, such as CT and MRI, is a significant aspect of clinical workflows in radiology and oncology treatment planning. With the advance of AI and high-performance computing, many methods and tools have been developed over the past decade and a half. However, end-to-end integration of solutions with existing workflows and practices remains a challenge. Here, we focus on the need for segmentation of anatomical structures whose delineations are clinically defined by a combination of anatomy, function, and treatment planning. Existing deep learning approaches for segmentation often struggle to effectively differentiate closely placed organs, such as the bladder, rectum, and sigmoid colon. We propose an efficient and robust, organ-specific segmentation pipeline based on tailored 2D U-Net models, coupled with anatomy-guided preprocessing and geometric postprocessing algorithms. We validate our method by a user study involving trained radiation oncologists, demonstrating high segmentation accuracy and significant reductions in contouring time. The results show that our approach produces consistently accurate contours that closely match expert delineations, with minimal corrections needed in clinical practice. This work highlights the benefits of deep learning integration in brachytherapy, enabling quicker planning and improved consistency through clinically validated organ segmentation.

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