Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.

Comput Intell Neurosci

Co-innovation Center of Neuroregeneration, Jiangsu Key Laboratory of Neuroregeneration, Nantong University, Nantong 226001, China.

Published: July 2019

AI Article Synopsis

  • Researchers have developed a dual deep learning framework called Dual ResUNet to enhance the segmentation of fluorescent vessels in zebrafish embryos, which is challenging due to 3D projection complexities.
  • The framework improves upon the traditional U-Net model by integrating domain knowledge with additional features like contour terms and shape constraints, maintaining important spatial information.
  • Experimental results demonstrate that the Dual ResUNet outperforms existing segmentation methods, proving effective even in difficult cases such as overlapping blood vessels or cases with insufficient fluorescent protein.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378085PMC
http://dx.doi.org/10.1155/2019/8214975DOI Listing

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