CartiMorph: a framework for automated knee articular cartilage morphometrics
This work addresses knee osteoarthritis diagnosis by providing an automated tool for cartilage morphometrics, though it is incremental as it builds on existing deep learning and image processing methods.
The authors tackled the problem of automated knee cartilage analysis by developing CartiMorph, a framework that uses deep learning to generate quantitative metrics like thickness and cartilage loss, achieving strong correlations (e.g., Pearson's ρ up to 0.98) and low error (e.g., FCL deviation <8%).
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 $ρ\in [0.82,0.97]$), surface area ($ρ\in [0.82,0.98]$) and volume ($ρ\in [0.89,0.98]$) 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.