GOARS: Generalized organ-at-risk segmentation utilizing hierarchical learning architecture and multi-dimensional...

Accurate segmentation of organs-at-risk (OARs) is essential in radiotherapy to minimize damage to healthy tissues while targeting cancerous cells. Current manual delineation methods are time-consuming and subject to significant inter-observer variability, thus necessitating automated solutions. However, existing deep learning approaches face challenges due to diverse anatomical structures, low contrast in medical images, and class imbalance resulting from varying organ sizes. To address these issues, we propose a generalized OAR segmentation method (GOARS) that leverages a hierarchical learning architecture and multi-dimensional feature aggregation. GOARS employs a coarse-to-fine framework to precisely segment both large and small organs. Unlike prior methods that rely on separate networks for small organs, GOARS integrates this functionality into a unified network, enhanced by an adaptive ROI extraction strategy for small, overlapping organs. Additionally, GOARS combines 2D and 3D networks to construct a dual-structured fine-grained segmentation model, enabling effective extraction and aggregation of multi-dimensional features. Evaluated on three independent datasets, GOARS demonstrated robust performance in handling anatomical variability of OARs and achieving accurate segmentation. This method has the potential to advance the precision and reliability of radiotherapy planning.