Segmentation of spine Magnetic Resonance Images (MRIs) has become an indispensable process in the diagnosis of lumbar disc degeneration, causing low back pain. Over the last decade of years, computer-directed diagnosis of disease, as well as computer-guided spine surgery, is based on the two-dimensional (2D) analysis of mid-sagittal slice of MRI. This work proposes an automatic strategy to extract the 3D segmentation of the normal disc as well as degenerated lumbar intervertebral discs (IVDs) from T2-weighted Turbo Spin Echo MRI of the spine using Connected Component (CC) analysis algorithm and statistical shape analysis. The challenges faced by the IVD segmentation includes (i) partial volume effects (ii) intensity inhomogeneity (iii) grey level overlap of different soft tissues. The proposed method first pre-processes the dataset and enables it for the application of the CC algorithm. The CC (subsets of pixels of the disc) of the spine MRI is extracted and apply statistical shape analysis for the refinement of the segmentation results to detect IVDs. Experimental results of the proposed method show a robust segmentation, accomplishing the dice similarity index of 92.4% and thus achieving a low error rate. Other performance measures such as Precision, Accuracy, JaccardIdx, JaccardDist, Global Consistency Error, Variation of Information, etc were also evaluated. The algorithm is evaluated quantitatively using adequate experiments on a dataset of 15 MRI scans, of different scenarios such as healthy and degenerate disc and this proposed method is verified as a promising accurate method for the automatic segmentation of IVD.

中文翻译：

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从MR图像中有效分割腰椎间盘

脊柱磁共振图像（MRI）的分割已成为诊断腰椎间盘退变，引起下腰痛的必不可少的过程。在过去的十年中，疾病的计算机控制诊断以及计算机引导的脊柱外科手术是基于MRI矢状位中段的二维（2D）分析。这项工作提出了一种自动策略，可使用连接组件（CC）分析算法和统计形状分析从脊椎的T2加权Turbo Spin Echo MRI提取正常椎间盘以及退化的腰椎间盘（IVD）。IVD分割面临的挑战包括（i）部分体积效应（ii）强度不均匀（iii）不同软组织的灰度重叠。所提出的方法首先对数据集进行预处理，并将其用于CC算法的应用。提取脊柱MRI的CC（椎间盘像素集），并应用统计形状分析来完善分割结果以检测IVD。所提方法的实验结果表明，该算法具有很好的分割效果，骰子相似度指标达到92.4％，错误率低。还评估了其他性能指标，例如精度，准确性，JaccardIdx，JaccardDist，全局一致性错误，信息变化等。使用适当的实验，对15种MRI扫描数据集进行了定量实验，对算法进行了定量评估，该数据集针对不同情况（例如健康和退化的椎间盘）进行了验证，该方法被验证为IVD自动分割的有希望的准确方法。