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（Lumbar Spinal Stenosis）会通过压迫脊神经引发腰背痛，可通过测量患者腰椎的前后径及椎间孔宽度对该病症进行验证。本研究旨在开发一种全新的腰椎管狭窄症严重程度评估策略，即从患者腰椎磁共振成像（Magnetic Resonance Imaging, MRI）中自动计算上述距离参数。
我们的方法首先利用SegNet对T1加权及T2加权复合轴位MRI图像进行语义分割，将图像划分为6个感兴趣区域：其中包括3个核心感兴趣区域，即椎间盘（Intervertebral Disc）、后部结构（Posterior Element）及硬膜囊（Thecal Sac），以及3个辅助感兴趣区域——前后结构间区域（Area between Anterior and Posterior elements）。
随后，我们应用一种新型轮廓演化算法，以提升关键区域边界处的分割精度。在分割后的图像中，通过勾勒区域边界可定位9个解剖学标志点，进而计算前后径与椎间孔宽度。
本研究通过包含515名患者腰椎MRI影像的腰椎MRI数据集开展系列实验，以评估所提算法的性能。实验针对22种不同设置，将我们的轮廓演化算法与测地线活动轮廓（Geodesic Active Contour）及Chan-Vese方法的分割性能进行了对比。
研究发现，当我们的轮廓演化算法同时用于优化训练SegNet模型所用的标签图像与自动分割图像的精度时，方法效果最优。计算得到的右侧及左侧椎间孔距离与专家手动测量值的平均误差分别为0.28 mm（p = 0.92）和0.29 mm（p = 0.97）；计算得到的前后径与专家测量值的平均误差为0.90 mm（p = 0.92）。此外，在椎间盘突出严重程度的判定上，本方法与专家意见的吻合度达到96.7%。

本数据集包含实验所用的MATLAB源代码、优化后的标签图像及复合图像。此外还需使用以下额外数据集与源代码：
- 腰椎MRI数据集：http://dx.doi.org/10.17632/k57fr854j2.2
- 腰椎MRI放射科医师报告数据集：http://dx.doi.org/10.17632/s6bgczr8s2.2
- 用于腰椎MRI数据集的真值数据集构建、语义分割及评估的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