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

Journal: Osteoarthritis and cartilage
Published Date: Mar 24, 2025

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.

Authors

  • Jacob L Griffith
    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.
  • Justin Joseph
    School of Bioengineering, VIT Bhopal University, Sehore, Madhya Pradesh - 466114, India. Electronic address: josephjusti@gmail.com.
  • Andrew Jensen
    Department of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, FL, USA.
  • Scott Banks
    Department of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, FL, USA; Department of Orthopaedic Surgery and Sports Medicine, University of Florida, Gainesville, FL, USA.
  • Kyle D Allen
    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; Department of Mechanical and Aerospace Engineering at the University of Florida, Gainesville, FL, USA; Department of Orthopaedic Surgery and Sports Medicine, University of Florida, Gainesville, FL, USA. Electronic address: kyle.allen@bme.ufl.edu.

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