3D Densely Convolutional Networks for Volumetric Segmentation (2017-09-11T00:00:00.000000Z)

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.

Authors

Taesup Moon

5 papers

T. Bui

1 papers

Jitae Shin

1 papers

3D Densely Convolutional Networks for Volumetric Segmentation

TL;DR

Abstract

Authors

References15 items

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