Computational techniques in zebrafish image processing and analysis

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.

A Workflow to Track and Analyze Endothelial Migration During Vascular Development in Zebrafish Embryos Using Lightsheet Microscopy

Zebrafish allow unrivalled in vivo imaging of vascular development due to their optical translucency and the availability of transgenic lines which fluorescently label cells and tissues of interest. Advances in light sheet fluorescence microscopy allow longer and faster imaging of live embryos at higher resolutions than previously possible, which facilitates study of dynamic cellular and molecular mechanisms underlying vessel formation and function. Here we describe a workflow using lightsheet microscopy to quantify endothelial cell (EC) migration dynamics during vascular development. Tracking movement of EC nuclei and analyzing the properties of EC migration trajectories permit detailed studies of angiogenesis and vascular remodeling in different contexts.

Zebrafish as a tool in the study of sleep and memory-related disorders

Sleep is an evolutionarily conserved phenomenon, being an essential biological necessity for the learning process and memory consolidation. The brain displays two types of electrical activity during sleep: slow-wave activity or non-rapid eye movement (NREM) sleep and desynchronized brain wave activity or rapid eye movement (REM) sleep. There are many theories about "Why we need to sleep?" among them the synaptic homeostasis. This theory proposes that the role of sleep is the restoration of synaptic homeostasis, which is destabilized by synaptic strengthening triggered by learning during waking and by synaptogenesis during development. Sleep diminishes the plasticity load on neurons and other cells to normalize synaptic strength. In contrast, it re-establishes neuronal selectivity and the ability to learn, leading to the consolidation and integration of memories. The use of zebrafish as a tool to assess sleep and its disorders is growing, although sleep in this animal is not yet divided, for example, into REM and NREM states. However, zebrafish are known to have a regulated daytime circadian rhythm. Their sleep state is characterized by periods of inactivity accompanied by an increase in arousal threshold, preference for resting place, and the "rebound sleep effect" phenomenon, which causes an increased slow-wave activity after a forced waking period. In addition, drugs known to modulate sleep, such as melatonin, nootropics, and nicotine, have been tested in zebrafish. In this review, we discuss the use of zebrafish as a model to investigate sleep mechanisms and their regulation, demonstrating this species as a promising model for sleep research.

High throughput automated detection of axial malformations in Medaka embryo

Fish embryo models are widely used as screening tools to assess the efficacy and/or toxicity of chemicals. This assessment involves the analysis of embryo morphological abnormalities. In this article, we propose a multi-scale pipeline to allow automated classification of fish embryos (Medaka: Oryzias latipes) based on the presence or absence of spine malformations. The proposed pipeline relies on the acquisition of fish embryo 2D images, on feature extraction based on mathematical morphology operators and on machine learning classification. After image acquisition, segmentation tools are used to detect the embryo before analysing several morphological features. An approach based on machine learning is then applied to these features to automatically classify embryos according to the presence of axial malformations. We built and validated our learning model on 1459 images with a 10-fold cross-validation by comparison with the gold standard of 3D observations performed under a microscope by a trained operator. Our pipeline results in correct classification in 85% of the cases included in the database. This percentage is similar to the percentage of success of a trained human operator working on 2D images. The key benefit of our approach is the low computational cost of our image analysis pipeline, which guarantees optimal throughput analysis.

A novel non-rigid registration algorithm for zebrafish larval images

Precise three-dimensional mapping of a large number of gene expression patterns, neuronal types and connections to an anatomical reference helps us to understand the vertebrate brain and its development. Zebrafish has evolved as a model organism for such study. In this paper, we propose a novel non-rigid registration algorithm for volumetric zebrafish larval image datasets. A coarse affine registration using the L-BFGS algorithm is applied first on the moving dataset. We then divide this coarsely registered moving image and the reference image into a union of overlapping patches. Minimum weight bipartite graph matching algorithm is employed to find the correspondence between the two sets of patches. The corresponding patches are then registered using the diffeomorphic demons method with proper intra-patch regularization. For each voxel lying in the overlapping regions, we impose inter-patch regularization through a composite transformation obtained from the adjacent transformation fields. Experimental results on four multi-view confocal 3D datasets show the advantage of the proposed solution over the existing ViBE-Z software.

Comparative mode-of-action analysis following manual and automated phenotype detection in Xenopus laevis

Given the increasing utilization of phenotypic screens in drug discovery also the subsequent mechanism-of-action analysis gains increased attention.

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

ZebIAT, an image analysis tool for registering zebrafish embryos and quantifying cancer metastasis

Background

Zebrafish embryos have recently been established as a xenotransplantation model of the metastatic behaviour of primary human tumours. Current tools for automated data extraction from the microscope images are restrictive concerning the developmental stage of the embryos, usually require laborious manual image preprocessing, and, in general, cannot characterize the metastasis as a function of the internal organs.

Methods

We present a tool, ZebIAT, that allows both automatic or semi-automatic registration of the outer contour and inner organs of zebrafish embryos. ZebIAT provides a registration at different stages of development and an automatic analysis of cancer metastasis per organ, thus allowing to study cancer progression. The semi-automation relies on a graphical user interface.

Results

We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs. Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections. We also demonstrate the applicability of the tool to studies of cancer progression.

Conclusions

ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis. It should be of use in high-throughput studies of cancer metastasis in zebrafish embryos.