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

• URL: http://arxiv.org/abs/2511.12110v1
• Date: Sat, 15 Nov 2025 08:59:21 GMT
• Title: MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images
• Authors: Qinyue Tong, Ziqian Lu, Jun Liu, Rui Zuo, Zheming Lu,
• Abstract summary: We introduce Multi-Round Entity-Level Medical Reasoning (MEMR-Seg)<n>MEMR-Seg is a new task that requires generating segmentation masks through multi-round queries with entity-level reasoning.<n>We construct MR-MedSeg, a large-scale dataset of 177K multi-round medical segmentation dialogues, featuring entity-based reasoning across rounds.
• Score: 10.168003371332746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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