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

arXiv: https://arxiv.org/abs/1909.02799 · HTML: https://mlsanity.com/paper/1909.02799/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application](https://mlsanity.com/paper/2108.09535/) (2021)

## Abstract (arXiv)

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%.