  One of the key drawbacks of 3D convolutional neural networks for segmentation
is their memory footprint, which necessitates compromises in the network
architecture in order to fit into a given memory budget. Motivated by the
RevNet for image classification, we propose a partially reversible U-Net
architecture that reduces memory consumption substantially. The reversible
architecture allows us to exactly recover each layer's outputs from the
subsequent layer's ones, eliminating the need to store activations for
backpropagation. This alleviates the biggest memory bottleneck and enables very
deep (theoretically infinitely deep) 3D architectures. On the BraTS challenge
dataset, we demonstrate substantial memory savings. We further show that the
freed memory can be used for processing the whole field-of-view (FOV) instead
of patches. Increasing network depth led to higher segmentation accuracy while
growing the memory footprint only by a very small fraction, thanks to the
partially reversible architecture.
