We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

翻译：我们提出了CartiMorph，一个用于膝关节软骨自动形态测量的框架。该框架以图像为输入，生成软骨亚区域的定量指标，包括全层软骨缺失百分比（FCL）、平均厚度、表面积和体积。CartiMorph利用深度学习模型进行层次化图像特征表示。我们训练并验证了用于组织分割、模板构建及模板到图像配准的深度学习模型。建立了基于表面法向的软骨厚度映射、FCL估计以及基于规则的软骨分区方法。我们的软骨厚度图在薄区域和边缘区域显示出更小的误差。通过比较模型分割和人工分割获得的定量指标，评估了所采用分割模型的有效性。FCL测量的均方根偏差小于8%，且平均厚度（Pearson相关系数$\rho \in [0.82,0.97]$）、表面积（$\rho \in [0.82,0.98]$）和体积（$\rho \in [0.89,0.98]$）测量结果间存在强相关性。我们将FCL测量结果与先前研究进行比较，发现我们的测量结果与真实值的偏差更小。观察表明，与基于图谱的方法相比，所提出的基于规则的软骨分区方法性能更优。CartiMorph有望促进膝骨关节炎成像生物标志物的发现。