Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Phys Med. 2021 Sep;89:51-62. doi: 10.1016/j.ejmp.2021.07.030. Epub 2021 Aug 2.
Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper presents a fully-automated image processing pipeline which quantifies the SC volume of MRI images.
In the proposed pipeline, after conducting some pre-processing tasks, a deep convolutional network is utilized to segment the spinal cord cross-sectional area (SCCSA) of each slice. After full segmentation, certain extra slices interpolate between each two adjacent slices using the shape-based interpolation method. Then, a 3D model of the SC is reconstructed, and, by counting the voxels of it, the SC volume is calculated. The performance of the proposed method for the SCCSA segmentation is evaluated on 140 MRI images. Subsequently, to demonstrate the application of the proposed pipeline, we study the differentiations of SC atrophy between 38 Multiple Sclerosis (MS) and 25 Neuromyelitis Optica Spectrum Disorder (NMOSD) patients.
The experimental results of the SCCSA segmentation indicate that the proposed method, adapted by Mask R-CNN, presented the most satisfactory result with the average Dice coefficient of 0.96. For this method, statistical metrics including sensitivity, specificity, accuracy, and precision are 97.51%, 99.98%, 99.92%, and 98.04% respectively. Moreover, the t-test result (p-value = 0.00089) verified a significant difference between the SC atrophy of MS and NMOSD patients.
The pipeline efficiently quantifies the SC volume of MRI images and can be utilized as an affordable computer-aided tool for diagnostic purposes.
在 MRI 图像中对大脑和脊髓（SC）的各种解剖区域进行定量测量，可作为独特的生物标志物，用于评估中枢神经系统脱髓鞘疾病的进展和疗效。本文提出了一种全自动图像处理流水线，用于量化 MRI 图像的 SC 体积。
在提出的流水线中，在进行一些预处理任务后，使用深度卷积网络对每个切片的脊髓横截面积（SCCSA）进行分割。完成全部分割后，使用基于形状的插值方法在每两个相邻切片之间插值某些额外的切片。然后，重建 SC 的 3D 模型，并通过对其体素进行计数来计算 SC 体积。在 140 张 MRI 图像上评估了所提出的方法对 SCCSA 分割的性能。随后，为了展示所提出流水线的应用，我们研究了 38 例多发性硬化症（MS）和 25 例视神经脊髓炎谱系障碍（NMOSD）患者的 SC 萎缩差异。
SCCSA 分割的实验结果表明，通过 Mask R-CNN 适配的所提出方法的效果最佳，平均 Dice 系数为 0.96。对于该方法，包括灵敏度、特异性、准确性和精度在内的统计指标分别为 97.51%、99.98%、99.92%和 98.04%。此外，t 检验结果（p 值=0.00089）验证了 MS 和 NMOSD 患者 SC 萎缩之间存在显著差异。
该流水线能够高效地量化 MRI 图像的 SC 体积，并可用作具有成本效益的计算机辅助诊断工具。
