  Convolutional neural networks are state-of-the-art for various segmentation
tasks. While for 2D images these networks are also computationally efficient,
3D convolutions have huge storage requirements and require long training time.
To overcome this issue, we introduce a network structure for volumetric data
without 3D convolutional layers. The main idea is to include maximum intensity
projections from different directions to transform the volumetric data to a
sequence of images, where each image contains information of the full data. We
then apply 2D convolutions to these projection images and lift them again to
volumetric data using a trainable reconstruction algorithm.The proposed network
architecture has less storage requirements than network structures using 3D
convolutions. For a tested binary segmentation task, it even shows better
performance than the 3D U-net and can be trained much faster.
