MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images

Despite notable progress in text-guided medical image seg-mentation nowadays, these methods are limited to single-round dia-logues and fail to support multi-round reasoning, which is importantfor medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), anew task that requires generating segmentation masks through multi-round queries with entity-level reasoning, helping learners progressivelydevelop their understanding of medical knowledge. To support this task,we construct MR-MedSeg, a large-scale dataset of 177K multi-roundmedical segmentation dialogues, featuring entity-based reasoning acrossrounds. Furthermore, we propose MediRound, an effective baselinemodel designed for multi-round medical reasoning segmentation. To mit-igate the inherent error propagation within the chain-like pipeline ofmulti-round segmentation, we introduce a lightweight yet effective Judg-ment & Correction Mechanism during model inference. Experi-mental results demonstrate that our method effectively addresses theMEMR-Seg task and outperforms conventional medical referring seg-mentation methods. The project is available at https: // github. com/Edisonhimself/ MediRound .