Literature DB >> 24732429

Bioimage Informatics in the context of Drosophila research.

Florian Jug1, Tobias Pietzsch1, Stephan Preibisch2, Pavel Tomancak3.   

Abstract

Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.
Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Drosophila; Image analysis; Processing; Registration; Segmentation; Tracking

Mesh:

Year:  2014        PMID: 24732429     DOI: 10.1016/j.ymeth.2014.04.004

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  8 in total

1.  Collaborative analysis of multi-gigapixel imaging data using Cytomine.

Authors:  Raphaël Marée; Loïc Rollus; Benjamin Stévens; Renaud Hoyoux; Gilles Louppe; Rémy Vandaele; Jean-Michel Begon; Philipp Kainz; Pierre Geurts; Louis Wehenkel
Journal:  Bioinformatics       Date:  2016-01-10       Impact factor: 6.937

2.  Shaped 3D singular spectrum analysis for quantifying gene expression, with application to the early zebrafish embryo.

Authors:  Alex Shlemov; Nina Golyandina; David Holloway; Alexander Spirov
Journal:  Biomed Res Int       Date:  2015-10-01       Impact factor: 3.411

3.  FlyExpress 7: An Integrated Discovery Platform To Study Coexpressed Genes Using in Situ Hybridization Images in Drosophila.

Authors:  Sudhir Kumar; Charlotte Konikoff; Maxwell Sanderford; Li Liu; Stuart Newfeld; Jieping Ye; Rob J Kulathinal
Journal:  G3 (Bethesda)       Date:  2017-08-07       Impact factor: 3.154

4.  Robust extraction of quantitative structural information from high-variance histological images of livers from necropsied Soay sheep.

Authors:  Q Caudron; R Garnier; J G Pilkington; K A Watt; C Hansen; B T Grenfell; T Aboellail; A L Graham
Journal:  R Soc Open Sci       Date:  2017-07-19       Impact factor: 2.963

5.  Deep Learning-Based Retrieval System for Gigapixel Histopathology Cases and the Open Access Literature.

Authors:  Roger Schaer; Sebastian Otálora; Oscar Jimenez-Del-Toro; Manfredo Atzori; Henning Müller
Journal:  J Pathol Inform       Date:  2019-07-01

6.  Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila.

Authors:  Sebastian M Bernasek; Nicolás Peláez; Richard W Carthew; Neda Bagheri; Luís A N Amaral
Journal:  PLoS Comput Biol       Date:  2020-03-03       Impact factor: 4.475

7.  Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection.

Authors:  Artur Yakimovich; Moona Huttunen; Jerzy Samolej; Barbara Clough; Nagisa Yoshida; Serge Mostowy; Eva-Maria Frickel; Jason Mercer
Journal:  mSphere       Date:  2020-09-09       Impact factor: 4.389

8.  Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences.

Authors:  Eric Wait; Mark Winter; Chris Bjornsson; Erzsebet Kokovay; Yue Wang; Susan Goderie; Sally Temple; Andrew R Cohen
Journal:  BMC Bioinformatics       Date:  2014-10-03       Impact factor: 3.169

  8 in total

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