MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images
This work addresses the problem of enabling interactive, multi-round reasoning for medical professionals in segmentation tasks, representing an incremental advancement in user-driven medical imaging.
The paper tackles the lack of interactivity and multi-round reasoning in medical image segmentation by introducing a new task, MEMR-Seg, and a dataset MR-MedSeg with 177K dialogues, achieving improved performance over conventional methods.
Despite the progress in medical image segmentation, most existing methods remain task-specific and lack interactivity. Although recent text-prompt-based segmentation approaches enhance user-driven and reasoning-based segmentation, they remain confined to single-round dialogues and fail to perform multi-round reasoning. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), a new task that requires generating segmentation masks through multi-round queries with entity-level reasoning. To support this task, we construct MR-MedSeg, a large-scale dataset of 177K multi-round medical segmentation dialogues, featuring entity-based reasoning across rounds. Furthermore, we propose MediRound, an effective baseline model designed for multi-round medical reasoning segmentation. To mitigate the inherent error propagation in the chain-like pipeline of multi-round segmentation, we introduce a lightweight yet effective Judgment & Correction Mechanism during model inference. Experimental results demonstrate that our method effectively addresses the MEMR-Seg task and outperforms conventional medical referring segmentation methods.