RADAR: Text-Guided Medical Image Segmentation via Residual Aggregation and Dense Alignment Representations

Specifying segmentation targets via text descriptions provides flexible semantic conditioning for medical image segmentation. However, existing methods still struggle to robustly translate high-level textual semantics into accurate pixel-level spatial prompts. In this paper, we propose RADAR (Residual Aggregation and Dense Alignment Representations), a parameter-efficient framework for text-guided medical image segmentation. RADAR explicitly decouples and synergizes the medical semantic anchoring capabilities of BiomedCLIP with the prompt-driven mask decoding abilities of MedSAM2, achieving low-cost task adaptation via LoRA. Specifically, RADAR introduces Guided Residual Depth Aggregation, which adaptively aggregates historical layer features under semantic guidance to mitigate textual semantic injection lag and deep feature dilution. Furthermore, we propose Structured Dense Representation Alignment, which leverages a frozen BiomedCLIP to construct crop-level semantic anchors, namely target, boundary, and context, to enhance the local discriminative power of dense features and the reliability of automated prompts. Additionally, Multi-modal Prompt Generation is designed to transform cross-modal semantic responses into composite prompts consisting of text, points, and masks. Experimental results on three public medical image segmentation datasets demonstrate that RADAR achieves competitive performance while introducing merely 0.74M trainable parameters, validating the effectiveness of the proposed approach in parameter-efficient adaptation and the enhancement of cross-layer and local representations. The source code is provided in the supplementary material.