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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