CartiMorph: a framework for automated knee articular cartilage morphometrics

TL;DR

CartiMorph is an automated deep learning framework that quantitatively analyzes knee cartilage morphology from images, providing accurate metrics useful for osteoarthritis research.

Contribution

This paper introduces a novel deep learning-based framework for automated knee cartilage morphometrics, including segmentation, thickness mapping, and parcellation, with validated high accuracy.

Findings

Root-mean-squared deviation of FCL < 8%

Strong correlations for thickness, surface area, and volume measurements

Superior performance of rule-based parcellation over atlas-based methods

Abstract

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.