Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
This work addresses the need for faster and more consistent tumor delineation in clinical radiation therapy settings, representing an incremental improvement over existing methods.
The study tackled the problem of time-consuming and subjective manual segmentation of brain tumors in radiosurgery by developing a deep learning-based semiautomatic system, which accelerated the contouring process by 2.2 times on average and increased inter-rater agreement from 92.0% to 96.5%.
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%.