Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Serpong, Indonesia.
Dr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
PLoS One. 2020 Nov 2;15(11):e0241309. doi: 10.1371/journal.pone.0241309. eCollection 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.
腰椎管狭窄症通过对脊神经根施加压力引起下腰痛。这可以通过测量患者腰椎的前后径和椎间孔宽度来验证。我们的目标是通过自动计算患者腰椎 MRI 的这些距离来开发一种评估腰椎管狭窄症程度的新策略。我们的方法从使用 SegNet 对 T1 和 T2 加权复合轴向 MRI 图像进行语义分割开始,将图像分为六个感兴趣区域。它们由三个主要的感兴趣区域组成,即椎间盘、后元素和脊膜囊,以及三个辅助的感兴趣区域,包括前、后元素之间的区域。然后应用一种新的轮廓演化算法来提高沿重要区域边界分割结果的准确性。通过对包含 515 名患者 MRI 研究的腰椎 MRI 数据集进行的一组实验来评估所提出算法的性能。这些实验比较了我们的轮廓演化算法与 Geodesic Active Contour 和 Chan-Vese 方法在 22 种不同设置下的性能。我们发现,当我们的轮廓演化算法应用于提高用于训练 SegNet 模型的标签图像和自动分割图像的准确性时,我们的方法效果最好。计算出的左右椎间孔距离相对于其专家测量距离的平均误差分别为 0.28 毫米(p = 0.92)和 0.29 毫米(p = 0.97)。计算出的前后径相对于其专家测量直径的平均误差为 0.90 毫米(p = 0.92)。该方法在确定椎间盘突出症的严重程度方面也与专家意见达成了 96.7%的一致性。