Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images
Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images
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DOI：
10.1007/s00586-016-4654-6
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发表时间：
2016-09-01
影响因子：
2.8
通讯作者：
Frangi, Alejandro F.
中科院分区：
文献类型：
--
作者：
Castro-Mateos, Isaac;Hua, Rui;Frangi, Alejandro F.
本文的主要目的是实现一种从MRI中自动、客观地计算Pfirmann‘s退变程度的方法。该分级系统用于腰痛(LBP)患者的诊断和治疗。此外，用于评估LBP患者治疗的生物力学模型需要该评分值来计算合适的材料特性。在这项工作中使用了48名患者的T2加权MR图像。240个腰椎IVD被分成训练组(140个)和测试组(100个)。三位专家使用Pfirmann的评分系统手动对整套静脉畸形进行分类，基本事实被选为其中投票最多的值。该方法使用活动轮廓模型来划定IVD的边界。随后，使用训练好的神经网络(NN)实现分类，所设计的8个特征包含静脉畸形的形状和强度信息。使用测试集对分类方法进行评估，其平均特异度(95.5%)和敏感度(87.3%)与每个专家对地面真实情况的平均特异度(95.5%)和敏感度(87.3%)相当。我们的结果表明，自动方法在分类精度方面与人类一样好。然而，人工注释具有固有的观察者间和观察者内的变异性，这导致了不一致的评估。相比之下，所提出的自动方法是客观的，只依赖于输入的MRI。
The primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann's degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties.T2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann's grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs.The classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth.Our results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI.