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.

翻译：形态学和功能成像中腰大肌的分析已被证明是评估肌少症（即可能与多因素病因学相关的系统性骨骼肌质量和功能丧失）的准确方法。将肌少症评估纳入放射学工作流程需要实现图像处理的计算流程，以保证分割可靠性和显著的自动化程度。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。具体而言，我们聚焦于水平集方法，比较了两种标准方法（经典演化模型和三维测地线模型）与后者的原始一阶改进模型的性能。分析结果表明，这些基于梯度的方案在手动分割方面具有可靠性，且一阶方案所需的计算负担显著小于二阶方法。