Keywords: Cartilage, JointsIn this study, we pre-trained a deep learning model for knee bone and cartilage segmentation with open dataset (MICCAI grand challenge "K2S 2022"), and then fine-tuned it with a small size of locally acquired data for customized task, in which we explored different contrasts as well. We can benefit from the large dataset size to increase the segmentation accuracy and generalization capabilities while reducing the labor and time of manual segmentation for training data. The preliminary results of a small dataset with 10 subjects using a simple 2D U-net are promising for single contrast and multi contrast images.
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