Rethinking RoI Strategy in Interactive 3D Segmentation for Medical Images

Ziyu Zhang, Yi Yu, Yuan Xue

Keywords: Interactive 3D segmentation, Region-of-Interest strategy, Medical image analysis

Abstract: 3D biomedical image segmentation is a critical technology for clinical diagnostics, surgical planning, and disease analysis. While foundation models such as SAM and its medical derivatives have achieved remarkable progress, their adaptation to volumetric images remains limited, particularly in terms of diverse imaging modalities and the need for efficient user interaction. To advance research in this field, CVPR 2025 Interactive 3D Biomedical Image Segmentation Challenge was established. We propose DCM (DualClickMed) as a solution to this challenge, with a dual-expert architecture featuring both global and local Region-of-Interest (RoI) strategies. The global-RoI expert provides comprehensive anatomical context by processing the entire organ based on user prompts, while the local-RoI expert focuses on high-resolution patches centered on specific user clicks, enabling precise segmentation of fine structures. We further introduce tailored prompt simulation strategies for each expert, closely mimicking real-world interactive behaviors during training. Extensive experiments on challenge dataset covering five modalities demonstrate that our approach outperforms baselines, with final DSC scores of 0.8533 (CT), 0.6880 (MRI), 0.6003 (Microscopy), 0.7864 (PET), and 0.9385 (Ultrasound), achieving significant improvements in both region overlap and boundary accuracy metrics.

Submission Number: 3