no code implementations • 25 Nov 2021 • Hadas Ben-Atya, Ori Rajchert, Liran Goshen, Moti Freiman
Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data plays an important role in assessing tumor response to therapy and personalized treatment stratification. Manual segmentation is tedious and subjective. Deep-learning-based algorithms for brain tumor segmentation have the potential to provide objective and fast tumor segmentation. However, the training of such algorithms requires large datasets which are not always available.