Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

Understand

Segmentation of 3D images is a fundamental problem in biomedical image analysis.

• Deep learning (DL) approaches have achieved state-of-the-art segmentation perfor- mance.
• To exploit the 3D contexts using neural networks, known DL segmentation methods, including 3D convolution, 2D convolution on planes orthogonal to 2D image slices, and LSTM in multiple directions, all suffer incompatibility with the highly anisotropic dimensions in common 3D biomedical images.
• In this paper, we propose a new DL framework for 3D image segmentation, based on a com- bination of a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively.