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