一个可跨部位、跨序列的MRI肌肉自动分割模型
SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
- 基于深度学习构建通用肌肉分割模型，支持多种MRI序列
- 在常见序列上达88.45%的骰子系数，异常情况仍保持86.21%
- 适合医学影像研究者用于肌肉量化分析，提升研究可重复性
肌肉数量与质量正被越来越多视为健康预测的重要指标。尽管MRI在评估中具有重要价值，但精准量化肌肉仍具挑战。本研究开发了一个公开可用的MRI肌肉分割模型，并验证其在不同解剖位置和成像序列中的适用性。数据来自单一三级中心（杜克大学医疗系统，2016–2020年）的160名患者共362例MRI，其中316例用于模型训练。模型在两组独立测试集上表现良好：一组包含28例常见序列，平均骰子相似系数（DSC）为88.45%；另一组含18例少见序列及肌肉萎缩、植入物、显著噪声等异常，DSC为86.21%。结果表明，该全自动深度学习算法可在多样环境中实现肌肉分割。模型公开发布，有助于推动肌肉与健康关系的标准化、可复现研究。
原文摘要 · Abstract (English)
The quantity and quality of muscles are increasingly recognized as important predictors of health outcomes. While MRI offers a valuable modality for such assessments, obtaining precise quantitative measurements of musculature remains challenging. This study aimed to develop a publicly available model for muscle segmentation in MRIs and demonstrate its applicability across various anatomical locations and imaging sequences. A total of 362 MRIs from 160 patients at a single tertiary center (Duke University Health System, 2016-2020) were included, with 316 MRIs from 114 patients used for model development. The model was tested on two separate sets: one with 28 MRIs representing common sequence types, achieving an average Dice Similarity Coefficient (DSC) of 88.45%, and another with 18 MRIs featuring less frequent sequences and abnormalities such as muscular atrophy, hardware, and significant noise, achieving 86.21% DSC. These results demonstrate the feasibility of a fully automated deep learning algorithm for segmenting muscles on MRI across diverse settings. The public release of this model enables consistent, reproducible research into the relationship between musculature and health.
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