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

## TL;DR

This paper introduces a projection-based 2.5D U-net architecture that efficiently segments volumetric data without 3D convolutions, reducing storage needs and training time while improving performance.

## Contribution

The novel architecture uses maximum intensity projections and a trainable reconstruction to avoid 3D convolutions, enhancing efficiency and effectiveness.

## Key findings

- Outperforms 3D U-net in segmentation accuracy
- Requires less storage and training time
- Achieves better results on tested binary segmentation task

## Abstract

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.