Articular cartilage in the tibiofemoral joint contains unique tissue microstructures that serve specific functions, including reduction of friction and distributing the dynamic and static cyclic loading at the ends of diarthrodial joints. A proficient understanding of these microstructures can lead to significant clinical advances in diagnosing orthopedic diseases such as osteoarthritis and improving cartilage repairs. The surface of tibiofemoral condyles can be roughly separated into loadbearing and meniscus-covered areas. Due to the difference in mechanical loading between the two regions, we hypothesize that their microstructures differ. To test this hypothesis, we used cartilage punches harvested from the tibial condyle of porcine knee joints as an example tissue and a custom nonlinear optical microscope for performing a dye-free imaging study. The custom nonlinear optical microscope could simultaneously acquire Two-Photon excitation Auto-Fluorescence (TPAF) and Second Harmonic Generation (SHG) images. Through the TPAF channel, elastin fibers are visible along with chondrocytes. The SHG channel was utilized for observing the vast collagen network and its evident orientation throughout the tibial condyle. Images were analyzed by ImageJ to reveal alignment angles of the collagen network and elastin fibers. The load-bearing region exhibits a denser uniform collagen network with minimum elastin fibers. In contrast, the meniscuscovered areas have a distinctive collagen orientation with a greater magnitude of co-localized elastin fibers. The biological differences are likely derived from their different biomechanical environments in the tibiofemoral joint.
Chondrocyte viability is an important measure to consider when assessing cartilage health. Dye-based cell viability assays are not suitable for in vivo or long-term studies. We have introduced a non-labeling viability assay based on the assessment of high-resolution images of cells and collagen structure using two-photon stimulated autofluorescence and second harmonic generation microscopy. By either the visual or quantitative assessment, we were able to differentiate living from dead chondrocytes in those images. However, both techniques require human participation and have limited throughputs. Throughput can be increased by using methods for automated cell-based image processing. Due to the poor image contrast, traditional image processing methods are ineffective on autofluorescence images produced by nonlinear microscopes. In this work, we examined chondrocyte segmentation and classification using Mask R-CNN, a deep learning approach to implement automated viability analysis. It has been demonstrated an 85% accuracy in chondrocyte viability assessment with proper training. This study demonstrates that automated and highly accurate image analysis is achievable with the use of deep learning methods. This image processing approach can be helpful to other imaging applications in clinical medicine and biological research.
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