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Abstract #0904

Segmentation of Brain Metastatic Lesions in Magnetic Resonance Imaging using Deep Learning

Jay B Patel1, Andrew L Beers1, Ken Chang1, James M Brown1, Katharina V Hoebel1, Bruce R Rosen1, Raymond Y Huang2, Priscilla Brastianos3, Elizabeth R Gerstner4, and Jayashree Kalpathy-Cramer1

1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Radiology, Brigham and Women’s Hospital, Boston, MA, United States, 3Massachusetts General Hospital, Boston, MA, United States, 4Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, MA, United States

Magnetic resonance imaging plays a key role in assessing the efficacy of treatment for patients with brain metastases by enabling neuroradiologists to track lesions sizes across time points. However, manual segmentation of multiple time-points is prohibitively time-consuming, thus precluding its use in current clinical workflow. In this study, we develop a deep learning approach to automatically segment metastatic lesions, and demonstrate that our predicted segmentation has high agreement with the gold-standard manual segmentation.

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