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Stereotactic radiosurgery (SRS), which delivers high doses of irradiation ina single or few shots to small targets, has been a standard of care for brainmetastases. While very effective, SRS currently requires manually intensivedelineation of tumors. In this work, we present a deep learning approach forautomated detection and segmentation of brain metastases using multimodalimaging and ensemble neural networks. In order to address small and multiplebrain metastases, we further propose a volume-aware Dice loss which optimizesmodel performance using the information of lesion size. This work surpassescurrent benchmark levels and demonstrates a reliable AI-assisted system for SRStreatment planning for multiple brain metastases.

Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery