Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model

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.

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

“…(d) Fluorescent vessel segmentation achieved by a dual deep learning framework, Dual ResUNet . Reprinted with permission from ref . Copyright 2019 Kun Zhang et al .…”

Embryo Spatial Model Reconstruction

“…UNet architecture was proposed for blastomere centroids localization [ 14 ]. Dual ResUNet model which avoids loss of spatial and identity was proposed for zebrafish embryo segmentation [ 15 ]. High speed camera and deep learning model are used for real time cell population analysis [ 16 ].…”

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

“…The open-source software Fiji was applied to segment transgenic fluorescent vasculature images (Schindelin et al, 2012;Kugler et al, 2019). A dual ResUNet model was proposed to detect more information for overlapping regions caused by an uneven distribution of fluorescence intensities (Zhang et al, 2019). Ip et al (2002) described an automated technique for segmenting, tracking, and identifying the tail vessels of zebrafish embryos using serial fluorescent images.…”