Studies on systematic evaluations of effects of activation functions and loss functions on deep learning-based automated knee compartments segmentation models are limited. In this work, we present a 2D-UNet model for simultaneous automated bone and cartilage segmentation, and analyze the effect of different activation functions (rectified linear unit[relu], sigmoid and softmax) at all or last layer, and different loss functions (categorical cross-entropy, multiclass dice coefficient loss) with and without surface distance weights, on model performance. The results showed significant performance differences in average surface distance (ASD) between different activation functions. Adding surface distance to loss functions improved segmentation performances.
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