Using deep-learning based segmentation to enable spatial evaluation of knee osteoarthritis (SEKO) in rodent models.

Abstract

OBJECTIVE: In preclinical models of osteoarthritis (OA), histology is commonly used to evaluate joint remodeling. The current study introduces a deep learning driven histological analysis pipeline for the spatial evaluation of knee osteoarthritis (SEKO) focused on quantifying and visualizing joint remodeling in the medial compartment of rodent knees. METHODS: The SEKO pipeline contains both segmentation and visualization tools. For segmentation, two separate convolutional neural network architectures, HRNet and U-Net, were considered for identifying multiple regions of interest. Following segmentation, SEKO calculates multiple morphometric and location dependent measures to summarize joint-level changes. Additionally, SEKO generates probabilistic heat maps for visualization of the spatial aspects of joint remodeling. RESULTS: SEKO incorporated the U-NET architecture - due to its higher prediction accuracy - and identified similar cartilage loss changes that were reported using by-hand segmentation in prior work. Additionally, SEKO enabled the detection of changes in subchondral bone area and location dependent bone remodeling. SEKO also enabled visualization of spatial changes in cartilage thinning and bone remodeling using probabilistic heat maps. CONCLUSION: The SEKO pipeline offers the potential for objective comparison of OA progression and therapeutic interventions through visualization of spatial and morphometric changes. SEKO is provided as an open-source tool for the OA research community, facilitating collaborative research efforts and comprehensive analysis of knee joint histology.