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Dense-U-Net: densely connected convolutional network for semantic segmentation with a small number of samples
Author(s): Yuanyi Zeng; Xiaoyu Chen; Yi Zhang; Lianfa Bai; Jing Han
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Paper Abstract

The main contribution of this work is the proposal of a densely connected convolutional network for semantic segmentation, which strengthens utilization of features and improves segmentation results even with limited training samples. To achieve this, we combine the U-Net network and our resulting system is called Dense-U-Net. Compared to traditional convolutional networks such as U-Net, there are additional concatenation layers between each pair of convolutional layers which have the same size of outputs in our Dense-U-Net, each layer can get the feature-maps of all its preceding layers as inputs while its feature-maps can be passed to all subsequent layers, and a higher segmentation quality can be achieved without a need for increasing the volume of datasets finally. We evaluate our proposed architecture by segmentation accuracy, foreground-restricted rand scoring after border thinning VRand and foreground-restricted information theoretic scoring after border thinning VInfo at the same time, and the results are shown on three different segmentation tasks: ISBI challenge 2012 for segmentation of neuronal structures in electron microscopic stacks, ISBI cell tracking challenge 2014(Glioblastoma-astrocytoma U373 cells) and 2015(HeLa cells), our Dense-U-Net achieves better results than U-Net and several other state-of-the-art networks on all tasks.

Paper Details

Date Published: 6 May 2019
PDF: 6 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692B (6 May 2019); doi: 10.1117/12.2524406
Show Author Affiliations
Yuanyi Zeng, Nanjing Univ. of Science and Technology (China)
Xiaoyu Chen, Nanjing Univ. of Science and Technology (China)
Yi Zhang, Nanjing Univ. of Science and Technology (China)
Lianfa Bai, Nanjing Univ. of Science and Technology (China)
Jing Han, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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