  Deep learning methods are actively used for brain lesion segmentation. One of
the most popular models is DeepMedic, which was developed for segmentation of
relatively large lesions like glioma and ischemic stroke. In our work, we
consider segmentation of brain tumors appropriate to stereotactic radiosurgery
which limits typical lesion sizes. These differences in target volumes lead to
a large number of false negatives (especially for small lesions) as well as to
an increased number of false positives for DeepMedic. We propose a new
patch-sampling procedure to increase network performance for small lesions. We
used a 6-year dataset from a stereotactic radiosurgery center. To evaluate our
approach, we conducted experiments with the three most frequent brain tumors:
metastasis, meningioma, schwannoma. In addition to cross-validation, we
estimated quality on a hold-out test set which was collected several years
later than the train one. The experimental results show solid improvements in
both cases.

Computer Vision and Pattern Recognition