Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics

TL;DR

This paper introduces CartiMorph Toolbox, a deep-learning pipeline for automated knee cartilage shape and lesion quantification from medical images, demonstrating competitive registration performance and providing a comprehensive analysis solution.

Contribution

It presents a novel deep-learning-based pipeline for fully automated knee cartilage morphometrics, integrating registration and shape analysis for osteoarthritis research.

Findings

Competitive registration results compared to state-of-the-art models

Automated pipeline for cartilage shape and lesion quantification

User-friendly software with visualization tools

Abstract

Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysis techniques like image segmentation, registration, and domain-specific image computing algorithms, only a few works focus on building fully automated pipelines for imaging feature extraction. In this study, we developed a deep-learning-based medical image analysis application for knee cartilage morphometrics, CartiMorph Toolbox (CMT). We proposed a 2-stage joint template learning and registration network, CMT-reg. We trained the model using the OAI-ZIB dataset and assessed its performance in template-to-image registration. The CMT-reg demonstrated competitive results compared to other state-of-the-art models. We integrated the proposed model into an automated pipeline for the quantification of cartilage shape and lesion (full-thickness cartilage loss, specifically). The toolbox provides a comprehensive, user-friendly solution for medical image analysis and data visualization. The software and models are available at https://github.com/YongchengYAO/CMT-AMAI24paper .