Amin Allalou

BlobFinder, a tool for fluorescence microscopy image cytometry

Images can be acquired at high rates with modern fluorescence microscopy hardware, giving rise to a demand for high-speed analysis of image data. Digital image cytometry, i.e., automated measurements and extraction of quantitative data from images of cells, provides valuable information for many types of biomedical analysis. There exists a number of different image analysis software packages that can be programmed to perform a wide array of useful measurements. However, the multi-application capability often compromises the simplicity of the tool. Also, the gain in speed of analysis is often compromised by time spent learning complicated software. We provide a free software called BlobFinder that is intended for a limited type of application, making it easy to use, easy to learn and optimized for its particular task. BlobFinder can perform batch processing of image data and quantify as well as localize cells and point like source signals in fluorescence microscopy images, e.g., from FISH, in situ PLA and padlock probing, in a fast and easy way.

Fully automated cellular-resolution vertebrate screening platform with parallel animal processing

The zebrafish larva is an optically-transparent vertebrate model with complex organs that is widely used to study genetics, developmental biology, and to model various human diseases. In this article, we present a set of novel technologies that significantly increase the throughput and capabilities of our previously described vertebrate automated screening technology (VAST). We developed a robust multi-thread system that can simultaneously process multiple animals. System throughput is limited only by the image acquisition speed rather than by the fluidic or mechanical processes. We developed image recognition algorithms that fully automate manipulation of animals, including orienting and positioning regions of interest within the microscopes field of view. We also identified the optimal capillary materials for high-resolution, distortion-free, low-background imaging of zebrafish larvae.

High-throughput hyperdimensional vertebrate phenotyping

Most gene mutations and biologically active molecules cause complex responses in animals that cannot be predicted by cell culture models. Yet animal studies remain too slow and their analyses are often limited to only a few readouts. Here we demonstrate high-throughput optical projection tomography with micrometer resolution and hyperdimensional screening of entire vertebrates in tens of seconds using a simple fluidic system. Hundreds of independent morphological features and complex phenotypes are automatically captured in three dimensions with unprecedented speed and detail in semi-transparent zebrafish larvae. By clustering quantitative phenotypic signatures, we can detect and classify even subtle alterations in many biological processes simultaneously. We term our approach hyperdimensional in vivo phenotyping (HIP). To illustrate the power of HIP, we have analyzed the effects of several classes of teratogens on cartilage formation using 200 independent morphological measurements and identified similarities and differences that correlate well with their known mechanisms of actions in mammals.

A detailed analysis of 3D subcellular signal localization.

Detection and localization of fluorescent signals in relation to other subcellular structures is an important task in various biological studies. Many methods for analysis of fluorescence microscopy image data are limited to 2D. As cells are in fact 3D structures, there is a growing need for robust methods for analysis of 3D data. This article presents an approach for detecting point‐like fluorescent signals and analyzing their subnuclear position. Cell nuclei are delineated using marker‐controlled (seeded) 3D watershed segmentation. User‐defined object and background seeds are given as input, and gradient information defines merging and splitting criteria. Point‐like signals are detected using a modified stable wave detector and localized in relation to the nuclear membrane using distance shells. The method was applied to a set of biological data studying the localization of Smad2‐Smad4 protein complexes in relation to the nuclear membrane. Smad complexes appear as early as 1 min after stimulation while the highest signal concentration is observed 45 min after stimulation, followed by a concentration decrease. The robust 3D signal detection and concentration measures obtained using the proposed method agree with previous observations while also revealing new information regarding the complex formation.

Image based measurements of single cell mtDNA mutation load

Accurate Interpolation in Appearance-Based Pose Estimation.- Automatic Segmentation of Overlapping Fish Using Shape Priors.- Automatic Feature Point Correspondences and Shape Analysis with Missing Data and Outliers Using MDL.- Variational Segmentation of Image Sequences Using Deformable Shape Priors.- Real-Time Face Detection Using Illumination Invariant Features.- Face Detection Using Multiple Cues.- Individual Discriminative Face Recognition Models Based on Subsets of Features.- Occluded Facial Expression Tracking.- Model Based Cardiac Motion Tracking Using Velocity Encoded Magnetic Resonance Imaging.- Fractal Analysis of Mammograms.- Reconstructing Teeth with Bite Information.- Sparse Statistical Deformation Model for the Analysis of Craniofacial Malformations in the Crouzon Mouse.- Monocular Point Based Pose Estimation of Artificial Markers by Using Evolutionary Computing.- Camera-to-Camera Mapping for Hybrid Pan-Tilt-Zoom Sensors Calibration.- Recursive Structure and Motion Estimation Based on Hybrid Matching Constraints.- Efficient Symmetry Detection Using Local Affine Frames.- Triangulation of Points, Lines and Conics.- Robust Variational Reconstruction from Multiple Views.- A Robust Approach for 3D Cars Reconstruction.- Novel Stereoscopic View Generation by Image-Based Rendering Coordinated with Depth Information.- Using Hidden Markov Models for Recognizing Action Primitives in Complex Actions.- Variational Segmentation Using Dynamical Models for Rigid Motion.- Context-Free Detection of Events.- Supporting Structure from Motion with a 3D-Range-Camera.- Object Recognition Using Frequency Domain Blur Invariant Features.- Regularized Neighborhood Component Analysis.- Finding the Minimum-Cost Path Without Cutting Corners.- Object Class Detection Using Local Image Features and Point Pattern Matching Constellation Search.- Image Segmentation with Context.- Improving Hyperspectral Classifiers: The Difference Between Reducing Data Dimensionality and Reducing Classifier Parameter Complexity.- A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images.- A Framework for Multiclass Reject in ECOC Classification Systems.- Scale-Space Texture Classification Using Combined Classifiers.- Multiresolution Approach in Computing NTF.- Generation and Empirical Investigation of hv-Convex Discrete Sets.- The Statistical Properties of Local Log-Contrast in Natural Images.- A Novel Parameter Decomposition Approach for Recovering Poses of Distal Locking Holes from Single Calibrated Fluoroscopic Image.- Covariance Estimation for SAD Block Matching.- Infrared-Visual Image Registration Based on Corners and Hausdorff Distance.- Watertight Multi-view Reconstruction Based on Volumetric Graph-Cuts.- Grain Size Measurement of Crystalline Products Using Maximum Difference Method.- Robust Boundary Delineation Using Random-Phase-Shift Active Contours.- Accurate Spatial Neighborhood Relationships for Arbitrarily-Shaped Objects Using Hamilton-Jacobi GVD.- FyFont: Find-your-Font in Large Font Databases.- Efficiently Capturing Object Contours for Non-Photorealistic Rendering.- Weighted Distances Based on Neighbourhood Sequences in Non-standard Three-Dimensional Grids.- Unsupervised Perceptual Segmentation of Natural Color Images Using Fuzzy-Based Hierarchical Algorithm.- Line-Stepping for Shell Meshes.- Nonlinear Functionals in the Construction of Multiscale Affine Invariants.- A New Fuzzy Impulse Noise Detection Method for Colour Images.- On Reasoning over Tracking Events.- FPGA Implementation of kNN Classifier Based on Wavelet Transform and Partial Distance Search.- Affine Illumination Compensation for Multispectral Images.- GPU-Based Edge-Directed Image Interpolation.- Graph-Based Range Image Registration Combining Geometric and Photometric Features.- Automatic Identification and Validation of Tie Points on Hyperspectral Satellite Images from CHRIS/PROBA.- Boneless Pose Editing and Animation.- Text Driven Face-Video Synthesis Using GMM and Spatial Correlation.- Accurate 3D Left-Right Brain Hemisphere Segmentation in MR Images Based on Shape Bottlenecks and Partial Volume Estimation.- Image Inpainting by Cooling and Heating.- Evaluating a General Class of Filters for Image Denoising.- Efficient Feature Extraction for Fast Segmentation of MR Brain Images.- Automated Mottling Assessment of Colored Printed Areas.- Image Based Measurements of Single Cell mtDNA Mutation Load.- A PCA-Based Technique to Detect Moving Objects.- Page Frame Detection for Marginal Noise Removal from Scanned Documents.- Representing Pairs of Orientations in the Plane.- Improved Chamfer Matching Using Interpolated Chamfer Distance and Subpixel Search.- Automatic Segmentation of Fibroglandular Tissue.- Temporal Bayesian Networks for Scenario Recognition.- Comparison of Combining Methods of Correlation Kernels in kPCA and kCCA for Texture Classification with Kansei Information.- A Visual System for Hand Gesture Recognition in Human-Computer Interaction.- Single View Motion Tracking by Depth and Silhouette Information.- Face Recognition with Irregular Region Spin Images.- Performance Evaluation of Adaptive Residual Interpolation, a Tool for Inter-layer Prediction in H.264/AVC Scalable Video Coding.- 3D Deformable Registration for Monitoring Radiotherapy Treatment in Prostate Cancer.- Reconstruction of 3D Curves for Quality Control.- Video Segmentation and Shot Boundary Detection Using Self-Organizing Maps.- Surface-to-Surface Registration Using Level Sets.- Multiple Object Tracking Via Multi-layer Multi-modal Framework.- Colorimetric and Multispectral Image Acquisition Using Model-Based and Empirical Device Characterization.- Robust Pseudo-hierarchical Support Vector Clustering.- Using Importance Sampling for Bayesian Feature Space Filtering.- Robust Moving Region Boundary Extraction Using Second Order Statistics.- A Linear Mapping for Stereo Triangulation.- Double Adaptive Filtering of Gaussian Noise Degraded Images.- Automatic Extraction and Classification of Vegetation Areas from High Resolution Images in Urban Areas.- An Intelligent Image Retrieval System Based on the Synergy of Color and Artificial Ant Colonies.- Filtering Video Volumes Using the Graphics Hardware.- Performance Comparison of Techniques for Approximating Image-Based Lighting by Directional Light Sources.- A Statistical Model of Head Asymmetry in Infants with Deformational Plagiocephaly.- Real-Time Visual Recognition of Objects and Scenes Using P-Channel Matching.- Graph Cut Based Segmentation of Soft Shadows for Seamless Removal and Augmentation.- Shadow Resistant Direct Image Registration.- Classification of Biological Objects Using Active Appearance Modelling and Color Cooccurrence Matrices.- Estimation of Non-Cartesian Local Structure Tensor Fields.- Similar Pattern Discrimination by Filter Mask Learning with Probabilistic Descent.- Robust Pose Estimation Using the SwissRanger SR-3000 Camera.- Pseudo-real Image Sequence Generator for Optical Flow Computations.Cell cultures as well as cells in tissue always display a certain degree of variability, and measurements based on cell averages will miss important information contained in a heterogeneous population. This paper presents automated methods for image based measurements of mitochondiral DNA (mtDNA) mutations in individual cells. The mitochondria are present in the cells cytoplasm, and each cytoplasm has to be delineated. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%. The final image based measurements of mitochondrial mutation load are also compared to, and show high agreement with, measurements made using biochemical techniques.

Robust signal detection in 3D fluorescence microscopy

Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point‐like signals on image data, often acquired in 3D. Visual detection of such point‐like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point‐source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point‐like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to visual detection by experts. The proposed method can be used as an efficient point‐like signal detection tool for various types of biological 3D image data.

Automated deep-phenotyping of the vertebrate brain

Here, we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex. DOI: http://dx.doi.org/10.7554/eLife.23379.001

Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion

ABSTRACT Objective: The research objective of the present investigation is to demonstrate the present status of passive in-vehicle driver breath alcohol detection and highlight the necessary conditions for large-scale implementation of such a system. Completely passive detection has remained a challenge mainly because of the requirements on signal resolution combined with the constraints of vehicle integration. The work is part of the Driver Alcohol Detection System for Safety (DADSS) program aiming at massive deployment of alcohol sensing systems that could potentially save thousands of American lives annually. Method: The work reported here builds on earlier investigations, in which it has been shown that detection of alcohol vapor in the proximity of a human subject may be traced to that subject by means of simultaneous recording of carbon dioxide (CO2) at the same location. Sensors based on infrared spectroscopy were developed to detect and quantify low concentrations of alcohol and CO2. In the present investigation, alcohol and CO2 were recorded at various locations in a vehicle cabin while human subjects were performing normal in-step procedures and driving preparations. A video camera directed to the driver position was recording images of the drivers upper body parts, including the face, and the images were analyzed with respect to features of significance to the breathing behavior and breath detection, such as mouth opening and head direction. Results: Improvement of the sensor system with respect to signal resolution including algorithm and software development, and fusion of the sensor and camera signals was successfully implemented and tested before starting the human study. In addition, experimental tests and simulations were performed with the purpose of connecting human subject data with repeatable experimental conditions. The results include occurrence statistics of detected breaths by signal peaks of CO2 and alcohol. From the statistical data, the accuracy of breath alcohol estimation and timing related to initial driver routines (door opening, taking a seat, door closure, buckling up, etc.) can be estimated. The investigation confirmed the feasibility of passive driver breath alcohol detection using our present system. Trade-offs between timing and sensor signal resolution requirements will become critical. Further improvement of sensor resolution and system ruggedness is required before the results can be industrialized. Conclusions: It is concluded that a further important step toward completely passive detection of driver breath alcohol has been taken. If required, the sniffer function with alcohol detection capability can be combined with a subsequent highly accurate breath test to confirm the drivers legal status using the same sensor device. The study is relevant to crash avoidance, in particular driver monitoring systems and driver–vehicle interface design.

Translating GWAS-identified loci for cardiac rhythm and rate using an in vivo, image-based, large-scale genetic screen in zebrafish

A meta-analysis of genome-wide association studies (GWAS) identified eight loci that are associated with heart rate variability (HRV), but candidate genes in these loci remain uncharacterized. We developed an image- and CRISPR/Cas9-based pipeline to systematically characterize candidate genes for HRV in live zebrafish embryos. Nine zebrafish orthologues of six human candidate genes were targeted simultaneously in eggs from fish that transgenically express GFP on smooth muscle cells (Tg[acta2:GFP]), to visualize the beating heart. An automated analysis of repeated 30s recordings of beating atria in 381 live, intact zebrafish embryos at 2 and 5 days post-fertilization highlighted genes that influence HRV (hcn4 and si:dkey-65j6.2 [KIAA1755]); heart rate (rgs6 and hcn4); and the risk of sinoatrial pauses and arrests (hcn4). Exposure to 10 or 25µM ivabradine – an open channel blocker of HCNs – for 24h resulted in a dose-dependent higher HRV and lower heart rate at 5 days post-fertilization. Hence, our screen confirmed the role of established genes for heart rate and rhythm (RGS6 and HCN4); showed that ivabradine reduces heart rate and increases HRV in zebrafish embryos, as it does in humans; and highlighted a novel gene that plays a role in HRV (KIAA1755).A meta-analysis of genome-wide association studies (GWAS) recently identified eight loci that are associated with heart rate variability (HRV) in data from 53,174 individuals. However, functional follow-up experiments - aiming to identify and characterize causal genes in these loci - have not yet been performed. We developed an image- and CRISPR-Cas9-based pipeline to systematically characterize candidate genes for HRV in live zebrafish embryos and larvae. Nine zebrafish orthologues of six human candidate genes were targeted simultaneously in fertilized eggs from fish that transgenically express GFP on smooth muscle cells (Tg( acta2:GFP )), to visualize the beating heart. An automated analysis of 30s recordings of 384 live zebrafish atria at 2 and 5 days post-fertilization helped identify genes that influence HRV ( kiaa1755 and gngt1 ); heart rate ( kiaa1755 ); sinoatrial pauses and arrests ( syt10 , hcn4 and kiaa1755 ); and early cardiac development ( gngt1 , neo1a ). Hence, comprehensively characterizing candidate genes in GWAS-identified loci for HRV in vivo helped us identify previously unanticipated culprits for life-threatening cardiac arrhythmias.

Decoding Gene Expression in 2D and 3D

Image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical “slicing” by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.