https://scholars.lib.ntu.edu.tw/handle/123456789/629517
標題: | Multimodal volume-aware detection and segmentation for brain metastases radiosurgery | 作者: | Szu-Yeu, Hu Weng, Wei-Hung Lu, Shao-Lun Cheng, Yueh-Hung FU-REN XIAO Hsu, Feng-Ming Lu, Jen-Tang |
關鍵字: | Brain Metastases;Deep learning;Radiosurgery | 公開日期: | 15-八月-2019 | 來源出版物: | arXiv | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/629517 | ISSN: | 23318422 |
顯示於: | 醫學系 |
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