Multimodal volume-aware detection and segmentation for brain metastases radiosurgery
Journal
arXiv
Date Issued
2019-08-15
Author(s)
Abstract
Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This work surpasses current benchmark levels and demonstrates a reliable AI-assisted system for SRS treatment planning for multiple brain metastases. Copyright © 2019, The Authors. All rights reserved.
Subjects
Brain Metastases
Deep learning
Radiosurgery
Type
other