Landmark-free morphometric analysis of knee osteoarthritis using joint statistical models of bone shape and articular space variability
Landmark-free morphometric analysis of knee osteoarthritis using joint statistical models of bone shape and articular space variability
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使用骨形状和关节空间变异性的关节统计模型对膝骨关节炎进行无地标形态测量分析
DOI：
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发表时间：
2021
影响因子：
2.4
通讯作者：
W. Zbijewski
中科院分区：
文献类型：
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作者：
N. Charon;Amanul Islam;W. Zbijewski
抽象的。目的：骨关节炎（OA）是一种常见的退行性疾病，涉及受影响关节的多种结构变化。除了关节间隙变窄之外，最近涉及统计形状分析方法的研究表明，特定的骨骼形状可能与该疾病有关。我们的目的是研究使用最近引入的功能形状框架（Fshape）来提取 OA 的形态特征的可行性，该特征将胫骨（或股骨）关节表面的形状变异性与关节空间的变化结合起来。方法：我们的研究使用 17 个无 OA 膝关节和 17 个有 OA 膝关节的三维锥束 CT 体积数据集。然后，每个膝盖被表示为一个对象（Fshape），该对象由三角化的胫骨（或股骨）关节表面和在该表面的点处测量的关节空间宽度（JSW）图（关节空间图，JSM）组成。我们引入了生成图谱模型来估计样本总体的模板（平均）Fshape 以及以模板为中心的变量，这些变量对从模板到每个受试者的转换进行建模。与膝关节 OA 中研究的其他统计形状建模方法相比，该方法具有两个潜在优势：（i）Fshapes 同时考虑骨骼形状和 JSW 的变异性，（ii）Fshape 图集估计基于表面的微分同胚变换模型，不需要受试者之间的先验地标对应关系。估计的图谱到受试者的 Fshape 变换用作主成分分析降维的输入，并结合线性支持向量机 (SVM) 分类器来识别 OA 的形态特征。结果：使用胫骨关节面作为 Fshape 的形状组件，我们发现仅基于骨表面变换的分类的留一交叉验证分数为 ≈91.18 % ，基于残余 JSM 的分类为 ≈91.18 % ，使用两者的分类为 ≈85.29 % Fshape 组件。使用股骨关节表面获得了类似的结果。统计分析中确定的判别方向与关节间隙内侧变窄、髁间隆起更陡以及内侧胫骨平台相对加深相关。结论：所提出的方法为形状和 JSP 的组合统计分析提供了一个集成框架。它可以成功提取与 OA 相关的特征，这些特征与该领域之前的研究一致。尽管未来需要进行大规模研究来证实这些发现的重要性，但我们的结果表明，功能形状方法是对 OA 和骨科数据进行形态学分析的一种有前途的新工具。
Abstract. Purpose: Osteoarthritis (OA) is a common degenerative disease involving a variety of structural changes in the affected joint. In addition to narrowing of the articular space, recent studies involving statistical shape analysis methods have suggested that specific bone shapes might be associated with the disease. We aim to investigate the feasibility of using the recently introduced framework of functional shapes (Fshape) to extract morphological features of OA that combine shape variability of articular surfaces of the tibia (or femur) together with the changes of the joint space. Approach: Our study uses a dataset of three-dimensional cone-beam CT volumes of 17 knees without OA and 17 knees with OA. Each knee is then represented as an object (Fshape) consisting of a triangulated tibial (or femoral) articular surface and a map of joint space widths (JSWs) measured at the points of this surface (joint space map, JSM). We introduce a generative atlas model to estimate a template (mean) Fshape of the sample population together with template-centered variables that model the transformations from the template to each subject. This approach has two potential advantages compared with other statistical shape modeling methods that have been investigated in knee OA: (i) Fshapes simultaneously consider the variability in bone shape and JSW, and (ii) Fshape atlas estimation is based on a diffeomorphic transformation model of surfaces that does not require a priori landmark correspondences between the subjects. The estimated atlas-to-subject Fshape transformations were used as input to principal component analysis dimensionality reduction combined with a linear support vector machine (SVM) classifier to identify the morphological features of OA. Results: Using tibial articular surface as the shape component of the Fshape, we found leave-one-out cross validation scores of ≈91.18 % for the classification based on the bone surface transformations alone, ≈91.18 % for the classification based on the residual JSM, and ≈85.29 % for the classification using both Fshape components. Similar results were obtained using femoral articular surfaces. The discriminant directions identified in the statistical analysis were associated with medial narrowing of the joint space, steeper intercondylar eminence, and relative deepening of the medial tibial plateau. Conclusions: The proposed approach provides an integrated framework for combined statistical analysis of shape and JSPs. It can successfully extract features correlated to OA that appear consistent with previous studies in the field. Although future large-scale study is necessary to confirm the significance of these findings, our results suggest that the functional shape methodology is a promising new tool for morphological analysis of OA and orthopedics data in general.