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Abstract #2457

Segmenting Brain Metastases Using Deep Learning on Multi-Modal MRI

Darvin Yi1, Endre Grøvik2,3, Michael Iv3, Elizabeth Tong3, Greg Zaharchuk3, and Daniel Rubin1

1Department of Biomedical Data Science, Stanford University, Stanford, CA, United States, 2Department for Diagnostic Physics, Oslo University Hospital, oslo, Norway, 3Department of Radiology, Stanford University, Stanford, CA, United States

Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-modal 3D imaging. Using deep learning to learn from the comprehensive pixel-wise labeled MRI-data, this work aims to train a fully convolution neural network for automatic detection and segmentation of brain metastases using multi-modal MRI. By training and testing on over 100 and 50 patients, respectively, including a variety of size and number of brain metastases from several primary cancers, this work provides a comprehensive investigation on the value and potential use of machine learning in a clinically relevant setting.

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