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arXiv · 1908.05418

Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Szu-Yeu Hu·Wei-Hung Weng·Shao-Lun Lu·Yueh-Hung Cheng·Furen Xiao·Feng-Ming Hsu·Jen-Tang Lu
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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.

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Szu-Yeu Hu, Wei-Hung Weng, Shao-Lun Lu, Yueh-Hung Cheng, Furen Xiao, Feng-Ming Hsu, Jen-Tang Lu. 2019-08-15. Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery. https://arxiv.org/abs/1908.05418

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