School of Information Science and Technology, Aichi Prefectural University, Nagakute, Japan.
Adv Exp Med Biol. 2020;1213:165-176. doi: 10.1007/978-3-030-33128-3_11.
Advancements in musculoskeletal analysis have been achieved by adopting deep learning technology in image recognition and analysis. Unlike musculoskeletal modeling based on computational anatomy, deep learning-based methods can obtain muscle information automatically. Through analysis of image features, both approaches can obtain muscle characteristics such as shape, volume, and area, and derive additional information by analyzing other image textures. In this chapter, we first discuss the necessity of musculoskeletal analysis and the required image processing technology. Then, the limitations of skeletal muscle recognition based on conventional handcrafted features are discussed, and developments in skeletal muscle recognition using machine learning and deep learning technology are described. Next, a technique for analyzing musculoskeletal systems using whole-body computed tomography (CT) images is shown. This study aims to achieve automatic recognition of skeletal muscles throughout the body and automatic classification of atrophic muscular disease using only image features, to demonstrate an application of whole-body musculoskeletal analysis driven by deep learning. Finally, we discuss future development of musculoskeletal analysis that effectively combines deep learning with handcrafted feature-based modeling techniques.
通过在图像识别和分析中采用深度学习技术，肌肉骨骼分析取得了进展。与基于计算解剖学的肌肉骨骼建模不同，基于深度学习的方法可以自动获取肌肉信息。通过分析图像特征，这两种方法都可以获得肌肉的形状、体积和面积等特征，并通过分析其他图像纹理来获得其他信息。在本章中，我们首先讨论了肌肉骨骼分析的必要性和所需的图像处理技术。然后，讨论了基于传统手工特征的骨骼肌识别的局限性，并描述了使用机器学习和深度学习技术进行骨骼肌识别的发展。接下来，展示了一种使用全身计算机断层扫描（CT）图像分析肌肉骨骼系统的技术。本研究旨在仅使用图像特征实现全身骨骼肌的自动识别和萎缩性肌肉疾病的自动分类，展示由深度学习驱动的全身肌肉骨骼分析的应用。最后，我们讨论了将深度学习与基于手工特征的建模技术有效结合的肌肉骨骼分析的未来发展。
