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arXiv · 1709.03199

3D Densely Convolutional Networks for Volumetric Segmentation

Toan Duc Bui·Jitae Shin·Taesup Moon
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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.

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Toan Duc Bui, Jitae Shin, Taesup Moon. 2017-09-11. 3D Densely Convolutional Networks for Volumetric Segmentation. https://arxiv.org/abs/1709.03199

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