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

Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation