Language-guided medical image segmentation with target-informed multi-level contrastive alignments

Medical image segmentation is a fundamental task in numerous medical applications. Recently, language-guided segmentation has shown promise in medical scenarios where textual clinical reports are readily available as semantic guidance. Clinical reports contain diagnostic information provided by clinicians, which can provide auxiliary textual semantics to guide segmentation. However, existing language-guided segmentation methods neglect the inherent pattern gaps between image and text modalities, resulting in sub-optimal visual-language integration. Contrastive learning is a well-recognized approach to align image-text patterns, but it has not been optimized for medical image segmentation, where clinically meaningful semantics are often concentrated in localized target regions rather than the entire image. In this study, we propose TMCA, a Target-informed Multi-level Contrastive Alignment framework to bridge image-text pattern gaps for medical language-guided segmentation. The core innovation is to reformulate image-text contrastive alignment from conventional instance-level matching to segmentation-oriented semantic matching, where image-text samples are aligned according to their segmentation targets rather than merely whether they come from the same patient. Specifically, TMCA enables target-informed image-text alignments and fine-grained textual guidance by introducing: (i) a target-sensitive semantic distance module that utilizes target information for more granular image-text alignment modeling, (ii) a multi-level contrastive alignment strategy that directs fine-grained textual guidance to multi-scale image details, and (iii) a language-guided target enhancement module that reinforces attention to critical regions based on the aligned image-text patterns. Extensive experiments on four public benchmarks, involving three medical imaging modalities with clinical reports, show that TMCA enabled superior performance over state-of-the-art language-guided medical segmentation methods.