PUBLICATION
Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model
- Authors
- Zhang, K., Zhang, H., Zhou, H., Crookes, D., Li, L., Shao, Y., Liu, D.
- ID
- ZDB-PUB-190314-12
- Date
- 2019
- Source
- Computational intelligence and neuroscience 2019: 8214975 (Journal)
- Registered Authors
- Liu, Dong
- Keywords
- none
- MeSH Terms
-
- Algorithms
- Animals
- Blood Vessels/anatomy & histology*
- Blood Vessels/diagnostic imaging*
- Blood Vessels/embryology
- Embryo, Nonmammalian
- Models, Anatomic
- Neural Networks, Computer*
- Signal Processing, Computer-Assisted*
- Zebrafish
- PubMed
- 30863436 Full text @ Comput Intell Neurosci
Citation
Zhang, K., Zhang, H., Zhou, H., Crookes, D., Li, L., Shao, Y., Liu, D. (2019) Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model. Computational intelligence and neuroscience. 2019:8214975.
Abstract
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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping