MIDA, Dipartimento di Matematica, Università di Genova, via Dodecaneso 35, Genova, 16145, Italy.
IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, Genova, 16132, Italy.
BMC Med Imaging. 2024 Sep 19;24(1):251. doi: 10.1186/s12880-024-01423-0.
The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.
对腰大肌进行形态和功能成像分析已被证明是评估肌肉减少症（一种全身性的骨骼肌质量和功能丧失，可能与多种病因学方面相关）的一种准确方法。要将肌肉减少症评估纳入放射学工作流程，就需要实现用于图像处理的计算管道，以保证分割的可靠性和较高的自动化程度。本研究利用三维数值方案对低剂量 X 射线计算机断层扫描图像中的腰大肌进行分割。具体来说，我们专注于水平集方法，并比较了两种标准方法（经典演化模型和三维测地线模型）以及该模型的一个原始一阶修正的性能。该分析的结果表明，这些基于梯度的方法相对于手动分割具有可靠性，而一阶方法的计算负担明显小于二阶方法。
