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arXiv · 2603.08215

Skill-Evolving Grounded Reasoning for Free-Text Promptable 3D Medical Image Segmentation

Tongrui Zhang·Chenhui Wang·Yongming Li·Zhihao Chen·Xufeng Zhan·Hongming Shan
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Abstract

Free-text promptable 3D medical image segmentation offers an intuitive and clinically flexible interaction paradigm. However, current methods are highly sensitive to linguistic variability: minor changes in phrasing can cause substantial performance degradation despite identical clinical intent. Existing approaches attempt to improve robustness through stronger vision-language fusion or larger vocabularies, yet they lack mechanisms to consistently align ambiguous free-form expressions with anatomically grounded representations. We propose Skill-Evolving grounded Reasoning (SEER), a novel framework for free-text promptable 3D medical image segmentation that explicitly bridges linguistic variability and anatomical precision through a reasoning-driven design. First, we curate the SEER-Trace dataset, which pairs raw clinical requests with image-grounded, skill-tagged reasoning traces, establishing a reproducible benchmark. Second, SEER constructs an evidence-aligned target representation via a vision-language reasoning chain that verifies clinical intent against image-derived anatomical evidence, thereby enforcing semantic consistency before voxel-level decoding. Third, we introduce SEER-Loop, a dynamic skill-evolving strategy that distills high-reward reasoning trajectories into reusable skill artifacts and progressively integrates them into subsequent inference, enabling structured self-refinement and improved robustness to diverse linguistic expressions. Extensive experiments demonstrate superior performance of SEER over state-of-the-art baselines. Under linguistic perturbations, SEER reduces performance variance by 81.94% and improves worst-case Dice by 18.60%. Project page: https://seer-medseg.github.io.

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Tongrui Zhang, Chenhui Wang, Yongming Li, Zhihao Chen, Xufeng Zhan, Hongming Shan. 2026-06-14. Skill-Evolving Grounded Reasoning for Free-Text Promptable 3D Medical Image Segmentation. https://arxiv.org/abs/2603.08215

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