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Proceedings Paper

Blood vessels segmentation of hatching eggs based on fully convolutional networks
Author(s): Lei Geng; Ling Qiu; Jun Wu; Zhitao Xiao
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Paper 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.

Paper Details

Date Published: 10 April 2018
PDF: 7 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152C (10 April 2018); doi: 10.1117/12.2303407
Show Author Affiliations
Lei Geng, Tianjin Polytechnic Univ. (China)
Tianjin Key Lab. of Optoelectronic Detection and Systems (China)
Ling Qiu, Tianjin Polytechnic Univ. (China)
Tianjin Key Lab. of Optoelectronic Detection and Systems (China)
Jun Wu, Tianjin Polytechnic Univ. (China)
Tianjin Key Lab. of Optoelectronic Detection and Systems (China)
Zhitao Xiao, Tianjin Polytechnic Univ. (China)
Tianjin Key Lab. of Optoelectronic Detection and Systems (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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