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.
Bibliographical noteFunding Information:
.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.
© 2019 Kun Zhang et al.
Copyright 2019 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Computer Science(all)