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%.
@article{arxiv.1909.02799,
title = {Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation},
author = {Boris Shirokikh and Alexandra Dalechina and Alexey Shevtsov and Egor Krivov and Valery Kostjuchenko and Amayak Durgaryan and Mikhail Galkin and Ivan Osinov and Andrey Golanov and Mikhail Belyaev},
journal= {arXiv preprint arXiv:1909.02799},
year = {2019}
}