Comparison of 2D and 3D U-Nets for Organ Segmentation
Dongdong Gu, Zhong Xue · 2021
As a popular convolutional neural network, U-net developed by Ronneberger et al. at the University of Freiburg, Germany, has been widely applied in biomedical image segmentation. The network is an extension of the fully convolutional network with multi-resolution encoder and decoder structures and skip layers at each resolution. It can achieve effective segmentation results with a limited training dataset. Initially developed for multi-channel 2D images, U-net has been extended to segmenting 3D biomedical images and even multi-channel or serial 3D images by using 3D convolution kernels, down-sampling, and up-sampling blocks, and various activation functions. In this chapter, using the lung CT segmentation challenge datasets, the performance of organ segmentation is compared between 2D and 3D U-nets trained at different spatial resolutions. The network structures used herein provide intuitive, hands-on experiences about their advantages and disadvantages for multiple organ segmentation. The comparative study could be helpful to facilitate research and development of deep learning-based biomedical image segmentation algorithms.