  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.
