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

TL;DR

This study evaluates deep learning models for brain tumor segmentation in MRI, demonstrating improved speed and consistency in clinical radiosurgery settings for various tumor types.

Contribution

It compares multiple deep convolutional network architectures and identifies the most effective model for semi-automatic brain tumor segmentation in clinical practice.

Findings

Segmentation process accelerated by 2.2 times on average.

Inter-rater agreement increased from 92.0% to 96.5%.

Validated in a clinical radiation therapy department.

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%.