CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation

2026-09-07

AI Summary

本文提出CGSM模型，通过结合LLM生成和临床审核的概念指导肺部病灶分割，利用CVAM和CM-Decoder提高分割精度，解决现有方法对小病灶分割不准确的问题。

Abstract

Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches often lack task-specific semantic guidance, as text-based annotations typically offer coarse localization of lesions, leading to inadequate delineation of lesion boundaries and poor performance on small-scale lesions. To address this, we propose CGSM, a Concept-Guided Segmentation Model that integrates LLM-generated and clinically reviewed concepts into the segmentation process. Specifically, we design a Concept-Visual Alignment Module (CVAM) to activate relevant tokens within the concepts that align with visual features, enhancing the interaction between textual and visual information. In addition, we introduce a Concept Modulated Decoder (CM-Decoder), which uses concepts from CVAM as modulation signals to facilitate the adaptive fusion of image and text features, improving the segmentation accuracy. Extensive experiments on two public datasets show that CGSM achieves state-of-the-art performance, with results of 91.59% Dice and 84.49% mIoU on the QaTa-COV19 dataset, demonstrating its effectiveness in pulmonary lesion segmentation.

Problem

• pulmonary lesion segmentation
• semantic guidance
• text-based annotations
• lesion boundaries
• small-scale lesions

Innovation

• Concept-Guided Segmentation
• CVAM
• CM-Decoder
• LLM-generated concepts