Song Genshen, Zhou Ji, Wang Kang, Yao Demin, Chen Shiyao, Shi Yonghong
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.
Front Neurosci. 2023 Jun 5;17:1203823. doi: 10.3389/fnins.2023.1203823. eCollection 2023.
Sarcopenia is generally diagnosed by the total area of skeletal muscle in the CT axial slice located in the third lumbar (L3) vertebra. However, patients with severe liver cirrhosis cannot accurately obtain the corresponding total skeletal muscle because their abdominal muscles are squeezed, which affects the diagnosis of sarcopenia.
This study proposes a novel lumbar skeletal muscle network to automatically segment multi-regional skeletal muscle from CT images, and explores the relationship between cirrhotic sarcopenia and each skeletal muscle region.
This study utilizes the skeletal muscle characteristics of different spatial regions to improve the 2.5D U-Net enhanced by residual structure. Specifically, a 3D texture attention enhancement block is proposed to tackle the issue of blurred edges with similar intensities and poor segmentation between different skeletal muscle regions, which contains skeletal muscle shape and muscle fibre texture to spatially constrain the integrity of skeletal muscle region and alleviate the difficulty of identifying muscle boundaries in axial slices. Subsequentially, a 3D encoding branch is constructed in conjunction with a 2.5D U-Net, which segments the lumbar skeletal muscle in multiple L3-related axial CT slices into four regions. Furthermore, the diagnostic cut-off values of the L3 skeletal muscle index (L3SMI) are investigated for identifying cirrhotic sarcopenia in four muscle regions segmented from CT images of 98 patients with liver cirrhosis.
Our method is evaluated on 317 CT images using the five-fold cross-validation method. For the four skeletal muscle regions segmented in the images from the independent test set, the avg. DSC is 0.937 and the avg. surface distance is 0.558 mm. For sarcopenia diagnosis in 98 patients with liver cirrhosis, the cut-off values of Rectus Abdominis, Right Psoas, Left Psoas, and Paravertebral are 16.67, 4.14, 3.76, and 13.20 cm/m in females, and 22.51, 5.84, 6.10, and 17.28 cm/m in males, respectively.
The proposed method can segment four skeletal muscle regions related to the L3 vertebra with high accuracy. Furthermore, the analysis shows that the Rectus Abdominis region can be used to assist in the diagnosis of sarcopenia when the total muscle is not available.
肌肉减少症通常通过位于第三腰椎（L3）椎体的CT轴位切片中的骨骼肌总面积来诊断。然而，严重肝硬化患者由于其腹肌受到挤压，无法准确获取相应的骨骼肌总量，这影响了肌肉减少症的诊断。
本研究提出一种新型的腰椎骨骼肌网络，用于从CT图像中自动分割多区域骨骼肌，并探讨肝硬化性肌肉减少症与每个骨骼肌区域之间的关系。
本研究利用不同空间区域的骨骼肌特征来改进由残差结构增强的2.5D U-Net。具体而言，提出了一种3D纹理注意力增强块来解决强度相似导致的边缘模糊以及不同骨骼肌区域之间分割不佳的问题，该块包含骨骼肌形状和肌纤维纹理，以在空间上约束骨骼肌区域的完整性，并减轻在轴位切片中识别肌肉边界的难度。随后，结合2.5D U-Net构建一个3D编码分支，将多个与L3相关的轴位CT切片中的腰椎骨骼肌分割为四个区域。此外，研究了L3骨骼肌指数（L3SMI）在从98例肝硬化患者的CT图像分割出的四个肌肉区域中用于识别肝硬化性肌肉减少症的诊断临界值。
我们的方法使用五折交叉验证法在317张CT图像上进行评估。对于在独立测试集图像中分割出的四个骨骼肌区域，平均DSC为0.937，平均表面距离为0.558毫米。对于98例肝硬化患者的肌肉减少症诊断，女性腹直肌、右侧腰大肌、左侧腰大肌和椎旁肌的临界值分别为16.67、4.14、3.76和13.20厘米/米，男性分别为22.51、5.84、6.10和17.28厘米/米。
所提出的方法能够高精度地分割与L3椎体相关的四个骨骼肌区域。此外，分析表明当无法获取总肌肉量时，腹直肌区域可用于辅助肌肉减少症的诊断。
