Objective evaluation of chondrocyte density & cloning after joint injury using convolutional neural networks

Abstract

Variations in chondrocyte density and organization in cartilage histology sections are associated with osteoarthritis progression. Rapid, accurate quantification of these two features can facilitate the evaluation of cartilage health and advance the understanding of their significance. The goal of this work was to adapt deep-learning-based methods to detect articular chondrocytes and chondrocyte clones from safranin-O-stained cartilage to evaluate chondrocyte cellularity and organization. The U-net and "you-only-look-once" (YOLO) models were trained and validated for identifying chondrocytes and chondrocyte clones, respectively. Validated models were then used to quantify chondrocyte and clone density in talar cartilage from Yucatan minipigs sacrificed 1 week, 3, 6, and 12 months after fixation of an intra-articular fracture of the hock joint. There was excellent/good agreement between expert researchers and the developed models in identifying chondrocytes/clones (U-net: R² = 0.93, y = 0.90x-0.69; median F1 score: 0.87/YOLO: R² = 0.79, y = 0.95x; median F1 score: 0.67). Average chondrocyte density increased 1 week after fracture (from 774 to 856 cells/mm² ), decreased substantially 3 months after fracture (610 cells/mm² ), and slowly increased 6 and 12 months after fracture (638 and 683 cells/mm² , respectively). Average detected clone density 3, 6, and 12 months after fracture (11, 11, 9 clones/mm² ) was higher than the 4-5 clones/mm² detected in normal tissue or 1 week after fracture and show local increases in clone density that varied across the joint surface with time. The accurate evaluation of cartilage cellularity and organization provided by this deep learning approach will increase objectivity of cartilage injury and regeneration assessments.

Keywords: U-net; YOLO; articular cartilage histology; chondrocyte; deep learning.