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Abstract #4733

Automatic Detection and Segmentation of Brain Metastases using Deep Learning on Multi-Modal MRI: A Multi-Center Study

Endre Grøvik1,2, Darvin Yi3, Michael Iv2, Elizabeth Tong2, Kyrre Eeg Emblem1, Line Brennhaug Nilsen1, Cathrine Saxhaug4, Kari Dolven Jacobsen5, Åslaug Helland5, Daniel Rubin3, and Greg Zaharchuk2

1Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway, 2Department of Radiology, Stanford University, Stanford, CA, United States, 3Department of Biomedical Data Science, Stanford University, Stanford, CA, United States, 4Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, 5Department of Oncology, Oslo University Hospital, Oslo, Norway

In recent years, many deep learning approaches have been developed and tested for automatic segmentation of gliomas. However, few studies have shown its potential for use in patients with brain metastases. Deep learning may ultimately aid radiologists in the tedious and time-consuming task of lesion segmentation. The objective of this work is to assess the clinical potential and generalizability of a deep learning technique, by training and testing a convolutional neural network for segmenting brain metastases using multi-center data.

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