Griffith Jacob L, Joseph Justin, Jensen Andrew, Banks Scott, Allen Kyle D
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Pain Research & Intervention Center of Excellence (PRICE), University of Florida, Gainesville, FL, USA.
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Osteoarthritis Cartilage. 2025 Jun;33(6):703-709. doi: 10.1016/j.joca.2025.02.787. Epub 2025 Mar 24.
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.
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.
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.
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.
在骨关节炎（OA）的临床前模型中，组织学常用于评估关节重塑。本研究引入了一种深度学习驱动的组织学分析流程，即膝关节骨关节炎空间评估（SEKO），重点是量化和可视化啮齿动物膝关节内侧间室的关节重塑。
SEKO流程包含分割和可视化工具。对于分割，考虑了两种独立的卷积神经网络架构，即HRNet和U-Net，用于识别多个感兴趣区域。分割后，SEKO计算多个形态测量和位置相关指标，以总结关节水平的变化。此外，SEKO生成概率热图，用于可视化关节重塑的空间方面。
SEKO采用了U-Net架构——因其更高的预测准确性——并识别出与先前工作中手工分割报告的类似软骨损失变化。此外，SEKO能够检测软骨下骨面积的变化和位置相关的骨重塑。SEKO还能够使用概率热图可视化软骨变薄和骨重塑的空间变化。
SEKO流程通过可视化空间和形态测量变化，为客观比较OA进展和治疗干预提供了可能性。SEKO作为一种开源工具提供给OA研究社区，有助于合作研究工作和膝关节组织学的综合分析。
