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.
In the recent studies of cartilage imaging with nonlinear optical microscopy, we discovered that autofluorescence of chondrocytes provided useful information for the viability assessment of articular cartilage. However, one of the hurdles to apply this technology in research or clinical applications is the lack of image processing tools that can perform automated and cell-based analysis. In this report, we present our recent effort in the cell segmentation using deep learning algorithms with the second harmonic generation images. Two traditional segmentation methods, adaptive threshold, and watershed, were used to compare the outcomes of different methods. We found that deep learning algorithms did not show a significant advantage over the traditional methods. Once the cellular area is determined, the viability index is calculated as the intensity ratio between two autofluorescence channels in the cellular area. We found the viability index correlated well with the chondrocyte viability. Again, deep learning segmentation did not show a significant difference from the traditional segmentation methods in terms of the correlation.
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