Muscle volume is a useful quantitative biomarker in sports, but also for the follow-up of degenerative musculo-skelletal diseases. In addition to volume, other shape biomarkers can be extracted by segmenting the muscles of interest from medical images. Manual segmentation is still today the gold standard for such measurements despite being very time-consuming. We propose a method for automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance Images to assist such morphometric analysis. By their nature, the tissue of different muscles is undistinguishable when observed in MR Images. Thus, muscle segmentation algorithms cannot rely on appearance but only on contour cues. However, such contours are hard to detect and their thickness varies across subjects. To cope with the above challenges, we propose a segmentation approach based on a hybrid architecture, combining convolutional and visual transformer blocks. We investigate for the first time the behaviour of such hybrid architectures in the context of muscle segmentation for shape analysis. Considering the consistent anatomical muscle configuration, we rely on transformer blocks to capture the longrange relations between the muscles. To further exploit the anatomical priors, a second contribution of this work consists in adding a regularisation loss based on an adjacency matrix of plausible muscle neighbourhoods estimated from the training data. Our experimental results on a unique database of elite athletes show it is possible to train complex hybrid models from a relatively small database of large volumes, while the anatomical prior regularisation favours better predictions.

翻译：肌肉体积是运动医学中重要的定量生物标志物，也可用于退行性肌肉骨骼疾病的随访监测。除体积外，通过医学图像分割目标肌肉还能提取其他形态生物标志物。尽管手动分割极为耗时，但目前仍是此类测量的金标准。本文提出一种自动分割方法，用于在三维磁共振图像中分割下肢18块肌肉，以辅助上述形态测量分析。由于不同肌肉组织在磁共振图像中无法通过纹理差异辨别，肌肉分割算法不能依赖表观特征，只能依靠轮廓线索。然而，这些轮廓难以检测且其厚度在不同受试者间存在差异。为应对上述挑战，我们提出基于混合架构的分割方法，该架构融合了卷积模块与视觉Transformer模块。本研究首次探索了此类混合架构在肌肉形态分析分割任务中的表现。考虑到肌肉解剖结构的一致性，我们利用Transformer模块捕捉肌肉间的长程依赖关系。为进一步利用解剖先验知识，本文的第二项贡献在于：基于训练数据估计的肌肉邻接关系矩阵，添加了正则化损失函数。我们在精英运动员特有数据集上的实验结果表明，即使数据量较小且体积较大，仍可训练出复杂的混合模型，而解剖先验正则化能有效提升预测精度。