基于提示引导的胸部CT中间质性肺疾病交互式分割

中文摘要 AI 辅助

准确分割间质性肺疾病（ILD）模式对于定量疾病评估和纵向监测至关重要。然而，现有方法仍存在局限性，它们依赖密集标注且生成无法优化的静态预测，这推动了交互式方法的发展。尽管可提示模型在交互式分割中展现出应用前景，但其在ILD领域的适配仍未得到充分探索。为填补这一空白，我们研究了用于ILD优化的提示引导基础模型，并据我们所知，首次将MedSAM2适配到胸部CT的3D ILD交互式分割任务中。我们研究了三种微调策略和多种临床驱动的提示：边界框（BBox）、点、套索和涂鸦。在涵盖七种ILD模式及健康肺组织的数据集上，全模型微调表现最佳，相比基线方法平均Dice分数提升了4.7个百分点。BBox提示实现了最强性能，非原生的MedSAM2交互方式如套索和涂鸦提示也被证明是有效的。最后，我们提出并评估了一个概念验证端到端工作流程，其中MedSAM2由自动分割先验初始化，随后使用放射科医生的提示进行优化。模型权重和插件可在以下网址获取：this https URL。

英文摘要

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.

发表机构

• University of Bern（伯尔尼大学）
• Graduate School for Cellular and Biomedical Sciences（细胞与生物医学科学研究生院）
• Bern University Hospital（伯尔尼大学医院）
• Lucern Cantonal Hospital（卢塞恩州立医院）
• Lausanne University Hospital (CHUV) and University of Lausanne（洛桑大学医院（CHUV）与洛桑大学）

机构由 AI 辅助整理，请以论文原文为准。