Prompt-Guided Interactive Segmentation of Interstitial Lung Disease in Thoracic CT

2026-08-28

AI Summary

研究通过改进MedSAM2模型，利用提示引导的交互式方法提高胸CT中ILD病灶分割精度，采用多种提示策略并优化模型微调方式。

Abstract

Accurate segmentation of interstitial lung disease (ILD) patterns is essential for quantitative disease assessment and longitudinal monitoring. However, existing approaches remain limited by relying on dense annotations and producing static predictions that cannot be refined, motivating interactive approaches. While promptable models show promise in interactive segmentation, their adaptation to ILDs remains largely unexplored. To address this gap, we investigate prompt-guided foundation models for ILD refinement and present, to the best of our knowledge, the first adaptation of MedSAM2 for interactive 3D ILD segmentation on thoracic CT. We investigate three fine-tuning strategies and multiple clinically motivated prompts: bounding-boxes (BBox), point, lasso, and scribble. On a dataset spanning seven ILD patterns and healthy lung tissue, full model fine-tuning performed best, improving the average Dice score by 4.7 percentage points over MedSAM2.While BBox prompts achieve the strongest performance, non-native MedSAM2 interactions such as lasso and scribble prompts also prove effective. Finally, we present and evaluate a proof-of-concept end-to-end workflow in which MedSAM2 is initialized from an automatic segmentation prior and subsequently refined using radiologist prompts. Model weights and plug-ins made available at: https://github.com/AIHNlab/ILD-SemiSegTool.

Problem

• Interstitial Lung Disease
• Interactive Segmentation
• Thoracic CT
• Dense Annotations
• Prompt-guided

Innovation

• interactive 3D ILD segmentation
• MedSAM2 adaptation
• fine-tuning strategies
• clinically motivated prompts
• end-to-end workflow