“…They also differ in channels needed—both the nuclei and the membrane channels are always preferred for analysis, yet quite often the membrane channel is unavailable because of other experimental considerations. When cell shape can be qualitatively characterized, this information could be also incorporated to improve the segmentation quality (15, 21). These methods are mostly adjusted to specific imaging techniques and experimental conditions, often resulting in custom-made image processing methodologies; that, however, obscures a united comparison between these methodologies.…”
Section: Methodsmentioning
confidence: 99%
“…Realizing their limitations in capturing large displacement, complex mitosis events, and, most importantly, scalability, researchers are now switching to an association-based approach that is usually expressed as (integer) linear programming problem (31, 32). With the help of some powerful commercial solvers such as CPLEX and Gurobi, this approach has proven to be highly efficient in tracking even thousands of cells (21). To avoid tedious parameter tuning, an advanced machine learning technique has been developed, which automatically optimizes the tracking model using biologists' annotations of preferred tracks (33).…”
Advances in optical imaging technologies combined with the use of genetically encoded fluorescent proteins have enabled the visualization of stem cells over extensive periods of time in vivo and ex vivo. The generation of genetically encoded fluorescent protein reporters that are fused with subcellularly localized proteins, such as human histone H2B, has made it possible to direct fluorescent protein reporters to specific subcellular structures and identify single cells in complex populations. This facilitates the visualization of cellular behaviors such as division, movement, and apoptosis at a single-cell resolution and, in principle, allows the prospective and retrospective tracking towards determining the lineage of each cell.
“…They also differ in channels needed—both the nuclei and the membrane channels are always preferred for analysis, yet quite often the membrane channel is unavailable because of other experimental considerations. When cell shape can be qualitatively characterized, this information could be also incorporated to improve the segmentation quality (15, 21). These methods are mostly adjusted to specific imaging techniques and experimental conditions, often resulting in custom-made image processing methodologies; that, however, obscures a united comparison between these methodologies.…”
Section: Methodsmentioning
confidence: 99%
“…Realizing their limitations in capturing large displacement, complex mitosis events, and, most importantly, scalability, researchers are now switching to an association-based approach that is usually expressed as (integer) linear programming problem (31, 32). With the help of some powerful commercial solvers such as CPLEX and Gurobi, this approach has proven to be highly efficient in tracking even thousands of cells (21). To avoid tedious parameter tuning, an advanced machine learning technique has been developed, which automatically optimizes the tracking model using biologists' annotations of preferred tracks (33).…”
Advances in optical imaging technologies combined with the use of genetically encoded fluorescent proteins have enabled the visualization of stem cells over extensive periods of time in vivo and ex vivo. The generation of genetically encoded fluorescent protein reporters that are fused with subcellularly localized proteins, such as human histone H2B, has made it possible to direct fluorescent protein reporters to specific subcellular structures and identify single cells in complex populations. This facilitates the visualization of cellular behaviors such as division, movement, and apoptosis at a single-cell resolution and, in principle, allows the prospective and retrospective tracking towards determining the lineage of each cell.
“…It includes stand-alone software tools and web services, add-ons for generic tools, and image processing algorithms that are integrated into complete screening systems: DanioVision (Noldus, Inc.): an integrated commercial system for the tracking of zebrafish larvae, DeltR 48 : an automated pipeline for analysis of time-resolved LSFM images of zebrafish embryogenesis, IN Cell Investigator Zebrafish Analysis Plug-In (GE Healthcare Life Sciences): an add-on for the commercial IN Cell system containing preconfigured analysis modules for >50 assays and applications, LSRTrack 21 : a MATLAB add-one for tracking of zebrafish larvae (free for academic use), ViBE-Z…”
Section: Specific Tools For Application To the Zebrafish Modelmentioning
confidence: 99%
“…Reconstructing a full or even partial cell lineage from fluorescently labeled nuclei data is a first important goal. 41,48,58 The deployment of the cell lineage in space and time is the basis for major biological insights, including the identification of the polyclonal origin of organs, the spatial dispersion of cell clones, the rate of proliferation along the lineage, and regularities in the orientation of division planes. 30 …”
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 highthroughput/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.
“…Such analyses are of
fundamental importance to understanding the development of biological tissues, to
reconstructing functional defects in mutants and disease models and to quantitatively
dissecting the mechanisms underlying the cellular building plan of entire complex organisms
(Keller et al , 2008). However, many computational challenges are encountered when performing key tasks, such as
image registration, cell segmentation and cell tracking, in complex microscopy datasets
(Khairy et al , 2008; Li et al , 2007; Lou et al , 2011; Preibisch et al , 2010; Rubio-Guivernau et al , 2012). Fig.…”
Motivation: Optical flow is a key method used for quantitative motion
estimation of biological structures in light microscopy. It has also been used as a key
module in segmentation and tracking systems and is considered a mature technology in the
field of computer vision. However, most of the research focused on 2D natural images,
which are small in size and rich in edges and texture information. In contrast, 3D
time-lapse recordings of biological specimens comprise up to several terabytes of image
data and often exhibit complex object dynamics as well as blurring due to the
point-spread-function of the microscope. Thus, new approaches to optical flow are required
to improve performance for such data.Results: We solve optical flow in large 3D time-lapse microscopy datasets by
defining a Markov random field (MRF) over super-voxels in the foreground and applying
motion smoothness constraints between super-voxels instead of voxel-wise. This model is
tailored to the specific characteristics of light microscopy datasets: super-voxels help
registration in textureless areas, the MRF over super-voxels efficiently propagates motion
information between neighboring cells and the background subtraction and super-voxels
reduce the dimensionality of the problem by an order of magnitude. We validate our
approach on large 3D time-lapse datasets of Drosophila and zebrafish
development by analyzing cell motion patterns. We show that our approach is, on average,
10 × faster than commonly used optical flow implementations in the Insight Tool-Kit
(ITK) and reduces the average flow end point error by 50% in regions with complex
dynamic processes, such as cell divisions.Availability: Source code freely available in the Software section at
http://janelia.org/lab/keller-lab.Contact:
amatf@janelia.hhmi.org or kellerp@janelia.hhmi.orgSupplementary information:
Supplementary data are available at Bioinformatics
online.
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