  This work investigates continual learning of two segmentation tasks in brain
MRI with neural networks. To explore in this context the capabilities of
current methods for countering catastrophic forgetting of the first task when a
new one is learned, we investigate elastic weight consolidation, a recently
proposed method based on Fisher information, originally evaluated on
reinforcement learning of Atari games. We use it to sequentially learn
segmentation of normal brain structures and then segmentation of white matter
lesions. Our findings show this recent method reduces catastrophic forgetting,
while large room for improvement exists in these challenging settings for
continual learning.
