Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning

Structural dynamics of calcified cartilage (CC) are poorly understood. Conventionally, CC structure is analyzed using histological sections. Micro-computed tomography (mu CT) allows for three-dimensional (3D) imaging of mineralized tissues; however, the segmentation between bone and mineralized cartilage is challenging. Here, we present state-of-the-art deep learning segmentation for mu CT images to assess 3D CC morphology. The sample includes 16 knees from 12 New Zealand White rabbits dissected into osteochondral samples from six anatomical regions: lateral and medial femoral condyles, lateral and medial tibial plateaus, femoral groove, and patella (n = 96). The samples were imaged with mu CT and processed for conventional histology. Manually segmented CC from the images was used to train segmentation models with different encoder-decoder architectures. The models with the greatest out-of-fold evaluation Dice score were selected. CC thickness was compared across 24 regions, co-registered between the imaging modalities using Pearson correlation and Bland-Altman analyses. Finally, the anatomical CC thickness variation was assessed via a Linear Mixed Model analysis. The best segmentation models yielded average Dice of 0.891 and 0.807 for histology and mu CT segmentation, respectively. The correlation between the co-registered regions was strong (r = 0.897, bias = 21.9 mu m, standard deviation = 21.5 mu m). Finally, both methods could separate the CC thickness between the patella, femoral, and tibial regions (p < 0.001). As a conclusion, the proposed mu CT analysis allows for ex vivo 3D assessment of CC morphology. We demonstrated the biomedical relevance of the method by quantifying CC thickness in different anatomical regions with a varying mean thickness. CC was thickest in the patella and thinnest in the tibial plateau. Our method is relatively straightforward to implement into standard mu CT analysis pipelines, allowing the analysis of CC morphology. In future research, mu CT imaging might be preferable to histology, especially when analyzing dynamic changes in cartilage mineralization. It could also provide further understanding of 3D morphological changes that may occur in mineralized cartilage, such as thickening of the subchondral plate in osteoarthritis and other joint diseases.

Automated summary

The study utilized deep learning segmentation for mu CT images to assess 3D CC morphology. Analysis of samples from different anatomical regions revealed variation in CC thickness, with the thickest in the patella and thinnest in the tibial plateau. The proposed mu CT analysis provides a reliable method for ex vivo 3D assessment of CC morphology, with potential applications in understanding cartilage mineralization dynamics and joint diseases.