  Stereotactic radiosurgery is a minimally-invasive treatment option for a
large number of patients with intracranial tumors. As part of the therapy
treatment, accurate delineation of brain tumors is of great importance.
However, slice-by-slice manual segmentation on T1c MRI could be time-consuming
(especially for multiple metastases) and subjective. In our work, we compared
several deep convolutional networks architectures and training procedures and
evaluated the best model in a radiation therapy department for three types of
brain tumors: meningiomas, schwannomas and multiple brain metastases. The
developed semiautomatic segmentation system accelerates the contouring process
by 2.2 times on average and increases inter-rater agreement from 92.0% to
96.5%.
