Aichi Prefectural University, Nagakute, Japan.
Adv Exp Med Biol. 2018;1093:81-91. doi: 10.1007/978-981-13-1396-7_7.
Skeletal muscle segmentation techniques can help orthopedic interventions in various scenes. In this chapter, we describe two methods of skeletal muscle segmentation on 3D CT images. The first method is based on a computational anatomical model, and the second method is a deep learning-based method. The computational anatomy-based methods are modeling the muscle shape with its running and use it for segmentation. In the deep learning-based methods, the muscle regions are directly acquired automatically. Both approaches can obtain muscle regions including shape, area, volume, and some other image texture features. And it is desirable that the method be selected by the required orthopedic intervention. Here, we show each design philosophy and features of a representative method. We discuss the various examples of site-specific segmentation of skeletal muscle in non-contrast images using torso CT and whole-body CT including in cervical, thoracoabdominal, surface and deep muscles. And we also mention the possibility of application to orthopedic intervention.
骨骼肌肉分割技术可以帮助各种场景下的矫形干预。在本章中，我们描述了两种基于 3D CT 图像的骨骼肌肉分割方法。第一种方法基于计算解剖模型，第二种方法是基于深度学习的方法。基于计算解剖的方法是使用其运行来对肌肉形状进行建模，并将其用于分割。在基于深度学习的方法中，肌肉区域可以自动直接获取。这两种方法都可以获得包括形状、面积、体积和其他一些图像纹理特征在内的肌肉区域。并且希望通过所需的矫形干预来选择该方法。在这里，我们展示了每个代表性方法的设计理念和特点。我们讨论了使用躯干 CT 和全身 CT（包括颈椎、胸腹、表面和深部肌肉）对非对比图像进行特定部位骨骼肌肉分割的各种示例。并且还提到了将其应用于矫形干预的可能性。
