3D Densely Convolutional Networks for VolumetricSegmentation

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Authors Toan Duc Bui, Jitae Shin, Taesup Moon
Journal/Conference Name arXiv preprint 2017 9
Paper Category
Paper 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 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
Date of publication 2017
Code Programming Language Python

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