Blood vessels segmentation of hatching eggs based on fully convolutional networks

Lei Geng; Ling Qiu; Jun Wu; Zhitao Xiao

doi:10.1117/12.2303407

10 April 2018 Blood vessels segmentation of hatching eggs based on fully convolutional networks

Lei Geng, Ling Qiu, Jun Wu, Zhitao Xiao

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152C (2018) https://doi.org/10.1117/12.2303407
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

FCN, trained end-to-end, pixels-to-pixels, predict result of each pixel. It has been widely used for semantic segmentation. In order to realize the blood vessels segmentation of hatching eggs, a method based on FCN is proposed in this paper. The training datasets are composed of patches extracted from very few images to augment data. The network combines with lower layer and deconvolution to enables precise segmentation. The proposed method frees from the problem that training deep networks need large scale samples. Experimental results on hatching eggs demonstrate that this method can yield more accurate segmentation outputs than previous researches. It provides a convenient reference for fertility detection subsequently.

Citation Download Citation

Lei Geng, Ling Qiu, Jun Wu, and Zhitao Xiao "Blood vessels segmentation of hatching eggs based on fully convolutional networks", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152C (10 April 2018); https://doi.org/10.1117/12.2303407

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