Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11850)


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.


Brain metastases Radiosurgery Deep learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Massachusetts General HospitalBostonUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Department of OncologyNational Taiwan University HospitalTaipeiTaiwan
  4. 4.Vysioneer Inc.CambridgeUSA
  5. 5.Department of SurgeryNational Taiwan University HospitalTaipeiTaiwan

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