Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation

Boris Shirokikh; Alexandra Dalechina; Alexey Shevtsov; Egor Krivov; Valery Kostjuchenko; Amayak Durgaryan; Mikhail Galkin; Ivan Osinov; Andrey Golanov; Mikhail Belyaev

arXiv · 1909.02799

Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation

Boris Shirokikh·Alexandra Dalechina·Alexey Shevtsov·Egor Krivov·Valery Kostjuchenko·Amayak Durgaryan·Mikhail Galkin·Ivan Osinov·Andrey Golanov·Mikhail Belyaev

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Abstract

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%.

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Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov, Egor Krivov, Valery Kostjuchenko, Amayak Durgaryan, Mikhail Galkin, Ivan Osinov, Andrey Golanov, Mikhail Belyaev. 2019-09-06. Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation. https://arxiv.org/abs/1909.02799

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