SegTom: A 3D Volumetric Medical Image Segmentation Framework for Thoracoabdominal Multi-Organ Anatomical Structures

Accurate segmentation of thoracoabdominal anatomical structures in three-dimensional medical imaging modalities is fundamental for informed clinical decision-making across a wide array of medical disciplines. Current approaches often struggle to efficiently and comprehensively process this region's intricate and heterogeneous anatomical information, leading to suboptimal outcomes in diagnosis, treatment planning, and disease management. To address this challenge, we introduce SegTom, a novel volumetric segmentation framework equipped with a cutting-edge SegTom Block specifically engineered to effectively capture the complex anatomical representations inherent to the thoracoabdominal region. This SegTom Block incorporates a hierarchical anatomical-representation decomposition to facilitate efficient information exchange by decomposing the computationally intensive self-attention mechanism and cost-effectively aggregating the extracted representations. Rigorous validation of SegTom across nine diverse datasets, encompassing both computed tomography (CT) and magnetic resonance imaging (MRI) modalities, consistently demonstrates high performance across a broad spectrum of anatomical structures. Specifically, SegTom achieves a mean Dice similarity coefficient (DSC) of 87.29% for cardiac segmentation on the MM-WHS MRI dataset, 83.48% for multi-organ segmentation on the BTCV abdominal CT dataset, and 92.01% for airway segmentation on a dedicated CT dataset. Code: https://github.com/deepang-ai/SegTom.