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Allalou, Amin
Publications (10 of 15) Show all publications
Allalou, A., Wu, Y., Ghannad-Rezaie, M., Eimon, P. M. & Yanik, M. F. (2017). Automated deep-phenotyping of the vertebrate brain. eLIFE, 6, Article ID e23379.
Open this publication in new window or tab >>Automated deep-phenotyping of the vertebrate brain
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2017 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 6, article id e23379Article in journal (Refereed) Published
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-324637 (URN)10.7554/eLife.23379 (DOI)000401797500001 ()
Available from: 2017-04-13 Created: 2017-06-16 Last updated: 2017-11-29Bibliographically approved
Bombrun, M., Ranefall, P., Lindblad, J., Allalou, A., Partel, G., Solorzano, L., . . . Wählby, C. (2017). Decoding gene expression in 2D and 3D. In: Image Analysis: Part II. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 257-268). Springer
Open this publication in new window or tab >>Decoding gene expression in 2D and 3D
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2017 (English)In: Image Analysis: Part II, Springer, 2017, p. 257-268Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science ; 10270
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-333686 (URN)10.1007/978-3-319-59129-2_22 (DOI)000454360300022 ()978-3-319-59128-5 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Projects
TissueMaps
Funder
EU, European Research Council, 682810
Available from: 2017-05-19 Created: 2017-11-16 Last updated: 2019-02-27Bibliographically approved
Ljungblad, J., Hök, B., Allalou, A. & Pettersson, H. (2017). Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion. Traffic Injury Prevention, 18, S31-S36
Open this publication in new window or tab >>Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion
2017 (English)In: Traffic Injury Prevention, ISSN 1538-9588, E-ISSN 1538-957X, Vol. 18, p. S31-S36Article in journal (Refereed) Published
Abstract [en]

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 driver's 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 driver's legal status using the same sensor device. The study is relevant to crash avoidance, in particular driver monitoring systems and driver-vehicle interface design.

Keywords
Passive breath alcohol detection, infrared gas sensor, automotive safety, contactless measurement
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:uu:diva-326377 (URN)10.1080/15389588.2017.1312688 (DOI)000402076900005 ()28368660 (PubMedID)
Funder
Knowledge FoundationVINNOVA
Available from: 2017-07-07 Created: 2017-07-07 Last updated: 2017-07-07Bibliographically approved
Pardo-Martin, C., Allalou, A., Medina, J., Eimon, P. M., Wählby, C. & Yanik, M. F. (2013). High-throughput hyperdimensional vertebrate phenotyping. Nature Communications, 4, 1467
Open this publication in new window or tab >>High-throughput hyperdimensional vertebrate phenotyping
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2013 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 4, p. 1467-Article in journal (Refereed) Published
Abstract [en]

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 micrometre 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 semitransparent 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. To illustrate the power of hyperdimensional in vivo phenotyping, we have analysed 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.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-199556 (URN)10.1038/ncomms2475 (DOI)000316616400037 ()
Available from: 2013-05-07 Created: 2013-05-07 Last updated: 2019-03-21
Chang, T.-Y., Pardo-Martin, C., Allalou, A., Wählby, C. & Yanik, M. F. (2012). Fully automated cellular-resolution vertebrate screening platform with parallel animal processing. Lab on a Chip, 12(4), 711-716
Open this publication in new window or tab >>Fully automated cellular-resolution vertebrate screening platform with parallel animal processing
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2012 (English)In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 12, no 4, p. 711-716Article in journal (Refereed) Published
Abstract [en]

The zebrafish larva is an optically-transparent vertebrate model with complex organs that is widelyused to study genetics, developmental biology, and to model various human diseases. In this article, wepresent a set of novel technologies that significantly increase the throughput and capabilities of ourpreviously described vertebrate automated screening technology (VAST). We developed a robustmulti-thread system that can simultaneously process multiple animals. System throughput is limitedonly by the image acquisition speed rather than by the fluidic or mechanical processes. We developedimage recognition algorithms that fully automate manipulation of animals, including orienting andpositioning regions of interest within the microscope’s field of view. We also identified the optimalcapillary materials for high-resolution, distortion-free, low-background imaging of zebrafish larvae.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-159202 (URN)10.1039/c1lc20849g (DOI)000299380800007 ()
Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2018-01-12Bibliographically approved
Pardo-Martin, C., Chang, T.-Y., Allalou, A., Wählby, C. & Yanik, M. F. (2011). High-throughput cellular-resolution in vivo vertebrate screening. In: Proc. 15th International Conference on Miniaturized Systems for Chemistry and Life Sciences.
Open this publication in new window or tab >>High-throughput cellular-resolution in vivo vertebrate screening
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2011 (English)In: Proc. 15th International Conference on Miniaturized Systems for Chemistry and Life Sciences, 2011Conference paper, Published paper (Refereed)
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-159201 (URN)
Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2011-11-04
Clausson, C.-M., Allalou, A., Weibrecht, I., Mahmoudi, S., Farnebo, M., Landegren, U., . . . Söderberg, O. (2011). Increasing the dynamic range of in situ PLA. Nature Methods, 8(11), 892-893
Open this publication in new window or tab >>Increasing the dynamic range of in situ PLA
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2011 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 8, no 11, p. 892-893Article in journal, Editorial material (Refereed) Published
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-159199 (URN)10.1038/nmeth.1743 (DOI)000296891800004 ()
Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2017-12-08Bibliographically approved
Allalou, A. (2011). Methods for 2D and 3D Quantitative Microscopy of Biological Samples. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Methods for 2D and 3D Quantitative Microscopy of Biological Samples
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening. 

It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that  produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions. 

HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. p. 70
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 856
Keywords
Image analysis, cytmetry, model organism, zebrafish, screening
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-159196 (URN)978-91-554-8167-4 (ISBN)
Public defence
2011-11-11, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2011-10-21 Created: 2011-09-25 Last updated: 2014-07-21Bibliographically approved
Pinidiyaarachchi, A., Zieba, A., Allalou, A., Pardali, K. & Wählby, C. (2009). A detailed analysis of 3D subcellular signal localization. Cytometry Part A, 75A(4), 319-328
Open this publication in new window or tab >>A detailed analysis of 3D subcellular signal localization
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2009 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 75A, no 4, p. 319-328Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
3D image analysis, fluorescence signal segmentation, subcellular positioning, Smad detection
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-98014 (URN)10.1002/cyto.a.20663 (DOI)000264513800006 ()
Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2018-01-13Bibliographically approved
Allalou, A. & Wählby, C. (2009). BlobFinder, a tool for fluorescence microscopy image cytometry. Computer Methods and Programs in Biomedicine, 94(1), 58-65
Open this publication in new window or tab >>BlobFinder, a tool for fluorescence microscopy image cytometry
2009 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 94, no 1, p. 58-65Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
Image cytometry, Single cell analysis, FISH, Software
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-87971 (URN)10.1016/j.cmpb.2008.08.006 (DOI)000264282400006 ()18950895 (PubMedID)
Available from: 2009-01-22 Created: 2009-01-16 Last updated: 2018-06-26Bibliographically approved
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