MATLAB source code for automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis (PLoS ONE 2020)

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm (p = 0.92) and 0.29 mm (p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm (p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

This data consists of the MATLAB source code, improved label images, and composite images used in the experiment. Additional datasets and source code are necessary:
- The Lumbar Spine MRI Dataset http://dx.doi.org/10.17632/k57fr854j2.2
- The Radiologists Notes for Lumbar Spine MRI Dataset http://dx.doi.org/10.17632/s6bgczr8s2.2
- The MATLAB source code for developing Ground Truth Dataset, Semantic Segmentation, and Evaluation for the Lumbar Spine MRI Dataset http://dx.doi.org/10.17632/8cp2cp7km8.2
- The original (unmodified) Label Image Data for Lumbar Spine MRI Dataset http://dx.doi.org/10.17632/zbf6b4pttk.2

腰椎管狭窄症通过施加于脊髓神经的压力导致下背部疼痛。此症状可通过测量患者腰椎的矢状径和椎间孔宽度进行验证。本研究的宗旨在于开发一种新的评估腰椎管狭窄程度的方法，通过自动计算患者腰椎MRI中的上述距离来实现。本研究方法首先利用SegNet对T1-和T2加权合成轴位MRI图像进行语义分割，将图像划分为六个感兴趣区域，包括三个主要感兴趣区域：椎间盘、后部结构和硬脊膜囊，以及三个辅助感兴趣区域，包括前后部结构之间的区域。随后，应用一种创新的轮廓演化算法以提升分割结果在重要区域边界处的准确性。在测量矢状径和椎间孔宽度之前，通过描绘分割图像中发现的区域边界，确定了图像上的九个解剖标志点。所提出的算法性能通过在包含515位患者MRI研究的腰椎MRI数据集上的一系列实验进行评估。这些实验将本团队提出的轮廓演化算法与测地线主动轮廓和Chan-Vese方法在22种不同配置下的性能进行了比较。研究发现，当轮廓演化算法被用于优化训练SegNet模型的标签图像以及自动分割的图像的准确性时，该方法效果最佳。计算出的右侧和左侧椎间孔距离相对于专家测量值的平均误差分别为0.28毫米（p = 0.92）和0.29毫米（p = 0.97），矢状径的平均误差相对于专家测量值直径为0.90毫米（p = 0.92）。该方法在确定椎间盘突出严重程度方面也与专家意见达成96.7%的一致性。本数据集包含用于实验的MATLAB源代码、改进的标签图像和合成图像。此外，还需以下数据集和源代码：- 腰椎MRI数据集（http://dx.doi.org/10.17632/k57fr854j2.2）；- 腰椎MRI数据集的放射科医生笔记（http://dx.doi.org/10.17632/s6bgczr8s2.2）；- 开发腰椎MRI数据集地面真实数据、语义分割和评估的MATLAB源代码（http://dx.doi.org/10.17632/8cp2cp7km8.2）；- 腰椎MRI数据集的原始（未修改）标签图像数据（http://dx.doi.org/10.17632/zbf6b4pttk.2）。