Focus on Bio-Image Informatics pp 263-272

Part of the Advances in Anatomy, Embryology and Cell Biology book series (ADVSANAT, volume 219)

Bioimage Informatics for Big Data

  • Hanchuan Peng
  • Jie Zhou
  • Zhi Zhou
  • Alessandro Bria
  • Yujie Li
  • Dean Mark Kleissas
  • Nathan G. Drenkow
  • Brian Long
  • Xiaoxiao Liu
  • Hanbo Chen

Abstract

Bioimage informatics is a field wherein high-throughput image informatics methods are used to solve challenging scientific problems related to biology and medicine. When the image datasets become larger and more complicated, many conventional image analysis approaches are no longer applicable. Here, we discuss two critical challenges of large-scale bioimage informatics applications, namely, data accessibility and adaptive data analysis. We highlight case studies to show that these challenges can be tackled based on distributed image computing as well as machine learning of image examples in a multidimensional environment.

References

  1. Anastassiou C et al (2015) Project MindScope: inferring cortical function in the mouse visual system, PNAS (submitted)Google Scholar
  2. Bria A, Iannello G, Peng H (2015) An open-source Vaa3D plugin for real-time 3D visualization of Terabyte-sized volumetric image. International symposium on biomedical imaging: from nano to macro, pp 520–523Google Scholar
  3. Burns R et al (2013) The Open Connectome Project Data Cluster: scalable analysis and vision for high-throughput neuroscience. SSDBM 2013Google Scholar
  4. Chen H et al (2015) SmartTracing: self-learning based neuron reconstruction. Brain Informatics (submitted)Google Scholar
  5. Collman F et al (2015) Mapping synapses by conjugate light-electron array tomography. J Neurosci 35:5792–5807CrossRefPubMedPubMedCentralGoogle Scholar
  6. Danuser G (2011) Computer vision in cell biology. Cell 147:973–978CrossRefPubMedGoogle Scholar
  7. De Chaumont F et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696CrossRefPubMedGoogle Scholar
  8. Jenett A et al (2012) A GAL4-driver line resource for Drosophila neurobiology. Cell Rep 2:991–1001CrossRefPubMedPubMedCentralGoogle Scholar
  9. Jones TR et al (2009) Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci U S A 106(6):1826–1831CrossRefPubMedPubMedCentralGoogle Scholar
  10. Jug F et al (2014) Bioimage Informatics in the context of Drosophila research. Methods 68(1):60–73CrossRefPubMedGoogle Scholar
  11. Kasthuri N et al (2015) Saturated reconstruction of a volume of neocortex. Cell 162:648–661CrossRefPubMedGoogle Scholar
  12. Khmelinskii A et al (2012) Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nat Biotechnol 30:708–714CrossRefPubMedGoogle Scholar
  13. Kim J et al (2012) mGRASP enables mapping mammalian synaptic connectivity with light microscopy. Nat Methods 9:96–102CrossRefGoogle Scholar
  14. Kutsuna N et al (2012) Active learning framework with iterative clustering for bioimage classification. Nat Commun 3:1032CrossRefPubMedPubMedCentralGoogle Scholar
  15. Kvilekval K et al (2010) Bisque: a platform for bioimage analysis and management. Bioinformatics 26:544–552CrossRefPubMedGoogle Scholar
  16. Li X et al (2015) Interactive exemplar-based segmentation toolkit for biomedical image analysis. International symposium on biomedical imaging: from nano to macro, pp 168–171Google Scholar
  17. Long F et al (2009) A 3D digital atlas of C. elegans and its application to single-cell analyses. Nat Methods 6:667–672CrossRefPubMedPubMedCentralGoogle Scholar
  18. Luisi J et al (2011) The FARSIGHT trace editor: an open source tool for 3-D inspection and efficient pattern analysis aided editing of automated neuronal reconstructions. Neuroinformatics 9:305–315CrossRefPubMedGoogle Scholar
  19. Mancuso JJ et al (2013) Methods of dendritic spine detection: from Golgi to high-resolution optical imaging. Neuroscience 251:129–140CrossRefPubMedPubMedCentralGoogle Scholar
  20. Maree R et al (2013) A rich internet application for remote visualization and collaborative annotation of digital slides in histology and cytology. Diagn Pathol 8(S1):S26CrossRefPubMedCentralGoogle Scholar
  21. Martone ME et al (2002) A cell-centered database for electron tomographic data. J Struct Biol 138:145–155CrossRefPubMedGoogle Scholar
  22. Micheva KD et al (2010) Single-synapse analysis of a diverse synapse population: proteomic imaging methods and markers. Neuron 68:639–653CrossRefPubMedPubMedCentralGoogle Scholar
  23. Mikut R et al (2013) Automated processing of Zebrafish imaging data: a survey. Zebrafish 10(3):401–421CrossRefPubMedPubMedCentralGoogle Scholar
  24. Myers G (2012) Why bioimage informatics matters. Nat Methods 9:659–660CrossRefPubMedGoogle Scholar
  25. Orlov N et al (2008) WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recognit Lett 29:1684–1693CrossRefPubMedPubMedCentralGoogle Scholar
  26. Peng H (2008) Bioimage informatics: a new area of engineering biology. Bioinformatics 24:1827–1836CrossRefPubMedPubMedCentralGoogle Scholar
  27. Peng H et al (2010) V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat Biotechnol 28:348–353CrossRefPubMedPubMedCentralGoogle Scholar
  28. Peng H et al (2011) BrainAligner: 3D registration atlases of Drosophila brains. Nat Methods 8:493–498CrossRefPubMedPubMedCentralGoogle Scholar
  29. Peng H et al (2012) Bioimage informatics: a new category in bioinformatics. Bioinformatics 28:1057CrossRefPubMedPubMedCentralGoogle Scholar
  30. Peng H et al (2014a) Extensible visualization and analysis for multidimensional images using Vaa3D. Nat Protoc 9:193–208CrossRefPubMedGoogle Scholar
  31. Peng H et al (2014b) Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis. Nat Commun 5:4342PubMedPubMedCentralGoogle Scholar
  32. Peng H et al (2015a) BigNeuron: large-scale 3D neuron reconstruction from optical microscopy images. Neuron. doi:10.1016/j.neuron.2015.1006.1036 PubMedGoogle Scholar
  33. Peng H, Meijering E, Ascoli GA (2015b) From DIADEM to BigNeuron. Neuroinformatics 13:259–260CrossRefPubMedGoogle Scholar
  34. Qu L, Long F, Peng H (2015) 3-D registration of biological images and models: registration of microscopic images and its uses in segmentation and annotation. IEEE Signal Proc Mag 32:70–77CrossRefGoogle Scholar
  35. Saalfeld S et al (2009) CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25:1984–1986CrossRefPubMedPubMedCentralGoogle Scholar
  36. Sanders J et al (2015) Learning-guided automatic three dimensional synapse quantification for drosophila neurons. BMC Bioinformatics 16:177CrossRefPubMedPubMedCentralGoogle Scholar
  37. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675CrossRefPubMedGoogle Scholar
  38. Silvestri L et al (2013) Micron-scale resolution optical tomography of entire mouse brains with confocal light sheet microscopy. J Vis Exp 80:e50696, doi:50610.53791/50696Google Scholar
  39. Sommer C et al (2011) ilastik: interactive learning and segmentation toolkit. IEEE international symposium on biomedical imaging: from nano to macro, pp. 230–233Google Scholar
  40. Swedlow JR et al (2003) Informatics and quantitative analysis in biological imaging. Science 300:100–102CrossRefPubMedPubMedCentralGoogle Scholar
  41. Swedlow JR et al (2009) Bioimage informatics for experimental biology. Annu Rev Biophys 38:327–346CrossRefPubMedPubMedCentralGoogle Scholar
  42. Tomer R et al (2012) Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nat Methods 9:755–763CrossRefPubMedGoogle Scholar
  43. Weiler N et al (2014) Synaptic molecular imaging in spared and deprived columns of mouse barrel cortex with array tomography. Scientific Data 1, December 23 2014, p 140046Google Scholar
  44. Zhou J, Peng H (2011) Counting cells in 3D confocal images based on discriminative models. Proceedings of the 2nd ACM conference on bioinformatics, computational biology and biomedicine. ACM, pp 399–403Google Scholar
  45. Zhou J et al (2013a) Performance model selection for learning-based biological image analysis on a cluster. Proceedings of the international conference on bioinformatics, computational biology and biomedical informatics. ACM, pp 324–332Google Scholar
  46. Zhou J et al (2013b) BIOCAT: a pattern recognition platform for customizable biological image classification and annotation. BMC Bioinformatics 14:291. doi:210.1186/1471-2105-1114-1291Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hanchuan Peng
    • 1
  • Jie Zhou
    • 2
  • Zhi Zhou
    • 1
  • Alessandro Bria
    • 3
    • 4
  • Yujie Li
    • 1
    • 5
  • Dean Mark Kleissas
    • 6
  • Nathan G. Drenkow
    • 6
  • Brian Long
    • 1
  • Xiaoxiao Liu
    • 1
  • Hanbo Chen
    • 1
    • 5
  1. 1.Allen Institute for Brain ScienceSeattleUSA
  2. 2.Department of Computer ScienceNorthern Illinois UniversityDekalbUSA
  3. 3.Department of EngineeringUniversity Campus Bio-Medico of RomeRomeItaly
  4. 4.Department of Electrical and Information EngineeringUniversity of Cassino and L.M.CassinoItaly
  5. 5.Department of Computer ScienceUniversity of GeorgiaAthensUSA
  6. 6.Johns Hopkins University Applied Physics LaboratoryLaurelUSA

Personalised recommendations