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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