  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. To our best knowledge, this
is the first DL framework for 3D image segmentation that explicitly leverages
3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal
Structure Segmentation Challenge and in-house image stacks for 3D fungus
segmentation, our approach achieves promising results comparing to the known
DL-based 3D segmentation approaches.
