Dense-U-Net: densely connected convolutional network for semantic segmentation with a small number of samples

Yuanyi Zeng; Xiaoyu Chen; Yi Zhang; Lianfa Bai; Jing Han

doi:10.1117/12.2524406

6 May 2019 Dense-U-Net: densely connected convolutional network for semantic segmentation with a small number of samples

Yuanyi Zeng, Xiaoyu Chen, Yi Zhang, Lianfa Bai, Jing Han

Author Affiliations +

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110692B (2019) https://doi.org/10.1117/12.2524406
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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 V^Rand and foreground-restricted information theoretic scoring after border thinning V^Info 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.

Citation Download Citation

Yuanyi Zeng, Xiaoyu Chen, Yi Zhang, Lianfa Bai, and Jing Han "Dense-U-Net: densely connected convolutional network for semantic segmentation with a small number of samples", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692B (6 May 2019); https://doi.org/10.1117/12.2524406

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available