3D Densely Convolutional Networks for Volumetric Segmentation
Published in
arXiv.org(2017)External Links:
TL
TL;DR
A novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation that provides a dense connection between layers that aims to improve the information flow in the network.
Abstract
In the isointense stage, the accurate volumetric image segmentation is a challenging task due to the low contrast between tissues. In this paper, we propose a novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation. The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network. By concatenating features map of fine and coarse dense blocks, it allows capturing multi-scale contextual information. Experimental results demonstrate significant advantages of the proposed method over existing methods, in terms of both segmentation accuracy and parameter efficiency in MICCAI grand challenge on 6-month infant brain MRI segmentation.
References15 items
1
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
2
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
3
Densely Connected Convolutional Networks
4
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
5