  Semantic segmentation of medical images with deep learning models is rapidly
developed. In this study, we benchmarked state-of-the-art deep learning
segmentation algorithms on our clinical stereotactic radiosurgery dataset,
demonstrating the strengths and weaknesses of these algorithms in a fairly
practical scenario. In particular, we compared the model performances with
respect to their sampling method, model architecture, and the choice of loss
functions, identifying the suitable settings for their applications and
shedding light on the possible improvements.
