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Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResU-Net model

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posted on 2019-05-20, 13:01 authored by K Zhang, H Zhang, H Zhou, D Crookes, L Li, Y Shao, D Liu
Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.

Funding

.is work was supported by the National Natural Science Foundation of China (Nos. 81570447, 61671255, and 81870359), the Natural Science Foundation of Jiangsu Province, China, under Grant No. BK20170443, the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 17KJB520030. H. Zhou was supported by UK EPSRC under Grant EP/ N011074/1, Royal Society-Newton Advanced Fellowship under Grant NA160342, and European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement No. 720325.

History

Citation

Computational Intelligence and Neuroscience, 2019, 8214975

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

  • VoR (Version of Record)

Published in

Computational Intelligence and Neuroscience

Publisher

Hindawi

eissn

1687-5273

Acceptance date

2019-12-31

Copyright date

2019

Available date

2019-05-20

Publisher version

https://www.hindawi.com/journals/cin/2019/8214975/abs/

Notes

The zebrafish data used to support the findings of this study were supplied by the Co-innovation Center of Neuroregeneration, Jiangsu Key Laboratory of Neuroregeneration under license and so cannot be made freely available. Requests for access to these data should be made to Kun Zhang at zhangkun_nt@163.com.

Language

en

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