Deep Learning for Chondrocyte Identification in Automated Histological Analysis of Articular Cartilage.

Background: Histology-based methods are commonly used in osteoarthritis (OA) research because they provide detailed information about cartilage health at the cellular and tissue level. Computer-based cartilage scoring systems have previously been developed using standard image analysis techniques to give more objective and reliable evaluations of OA severity. The goal of this work was to develop a deep learning-based method to segment chondrocytes from histological images of cartilage and validate the resulting method via comparison with human segmentation.

Methods: The U-Net approach was adapted for the task of chondrocyte segmentation. A training dataset consisting of 235 images and a validation set consisting of 25 images in which individual chondrocytes had been manually segmented, were used for training the U-Net. Chondrocyte count, detection accuracy, and boundary segmentation of the trained U-Net was evaluated by comparing its results with those of human observers.

Results: The U-Net chondrocyte counts were not significantly different (p = 0.361 in a paired t-test) than the algorithm trainer counts (Pearson correlation coefficient = 0.92). The five expert observers had good agreement on chondrocyte counts (intraclass correlation coefficient = 0.868), however the resulting U-Net counted a significantly fewer chondrocytes than the average of those expert observers (p < 0.001 in a paired t-test). Chondrocytes were accurately detected by the U-Net (F1 scores = 0.86, 0.90, with respect to the selected expert observer and algorithm trainer). Segmentation accuracy was also high (IOU = 0.828) relative to the algorithm trainer.

Conclusions: This work developed a method for chondrocyte segmentation from histological images of arthritic cartilage using a deep learning approach. The resulting method detected chondrocytes and delineated them with high accuracy. The method will continue to be improved through expansion to detect more complex cellular features representative of OA such as cell cloning.

Clinical Relevance: The imaging tool developed in this work can be integrated into an automated cartilage health scoring system and helps provide a robust, objective and reliable assessment of OA severity in cartilage.