Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
This addresses segmentation challenges in biomedical imaging, offering a novel approach for anisotropic 3D images, though it appears incremental as it builds on existing methods.
The paper tackles 3D biomedical image segmentation by proposing a new deep learning framework combining fully convolutional and recurrent neural networks to handle anisotropic dimensions, achieving promising results on datasets like the ISBI Neuronal Structure Segmentation Challenge and in-house fungus segmentation.
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.