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Convolutional neural networks are state-of-the-art for various segmentationtasks. 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 datawithout 3D convolutional layers. The main idea is to include maximum intensityprojections from different directions to transform the volumetric data to asequence of images, where each image contains information of the full data. Wethen apply 2D convolutions to these projection images and lift them again tovolumetric data using a trainable reconstruction algorithm.The proposed networkarchitecture has less storage requirements than network structures using 3Dconvolutions. For a tested binary segmentation task, it even shows betterperformance than the 3D U-net and can be trained much faster.

Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation