Abstract #4733

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

Endre Grøvik^1,2, 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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