首个可公开使用的下肢CT组织分割与量化系统，精准识别肌肉和脂肪。
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification
- 基于1302张标注切片训练，端到端分割骨、肌肉及皮下/肌内脂肪。
- 在900张测试切片上平均Dice达89.31，优于多种2D模型。
- 适合医学影像研究者用于骨骼肌疾病分析与体成分评估。
下肢计算机断层扫描（CT）包含体成分分析、肌少症评估和骨科疾病监测的临床信息，但大规模提取这些指标需准确的组织分割与自动化量化流程。现有公开分割工具未针对下肢全面分析设计，尤其缺乏对临床关键的肌间/肌内脂肪的处理，多数仅提供掩码预测而非端到端量化系统。为此，我们提出LegSegNet，一个用于下肢CT组织分割与体成分量化的深度学习系统。输入CT扫描后，该系统分割骨、骨骼肌、皮下脂肪以及肌间/肌内脂肪，并计算定量测量值供后续分析。模型基于1,302张人工标注的CT切片训练，在900张保留测试切片上评估，所有标注经放射科医生审核。我们对比了多种2D分割方法，包括基于CNN、Transformer的模型及微调的基础模型，并进一步在外部公开数据集上评估其泛化能力。LegSegNet在测试集上平均Dice得分为89.31，表现最优。据我们所知，LegSegNet是首个公开可用的下肢CT组织分割与量化端到端系统，为医学图像分析领域的计算机视觉研究提供实用评估工具。代码与模型权重可在https://github.com/mazurowski-lab/LegSegNet获取。
原文摘要 · Abstract (English)
Lower extremity computed tomography (CT) contains clinically relevant information for body composition analysis, sarcopenia assessment, and musculoskeletal disease monitoring, but extracting these measurements at scale requires accurate tissue segmentation and an automated quantification workflow. Existing public segmentation tools are not designed for comprehensive lower extremity CT analysis, particularly for clinically important inter/intramuscular adipose tissue, and most public methods only provide mask prediction rather than an end-to-end quantification system. To address this problem, we present LegSegNet, a deep learning system for lower extremity CT tissue segmentation and body composition quantification. Given an input CT scan, LegSegNet segments bone, skeletal muscle, subcutaneous adipose tissue, and inter/intramuscular adipose tissue. It then computes quantitative tissue measurements for downstream analysis. We developed the segmentation model using 1,302 manually annotated CT slices and evaluated it on 900 held-out test slices, with all annotations reviewed by radiologists. We benchmark LegSegNet against a broad set of 2D segmentation methods, including CNN-based models, transformer-based models, and finetuned foundation models, and further evaluate its generalization on an external public CT dataset. LegSegNet achieves the best overall segmentation performance, with an average Dice score of 89.31 on the held-out test set. To our knowledge, LegSegNet is the first publicly available end-to-end system for lower extremity CT tissue segmentation and quantification, providing a practical evaluation tool for future computer vision research in medical image analysis. The code and model weights are available at: https://github.com/mazurowski-lab/LegSegNet
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