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 When the image datasets become larger and more complicated, many conventional image analysis approaches are no longer applicable.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.
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References
Anastassiou C et al (2015) Project MindScope: inferring cortical function in the mouse visual system, PNAS (submitted)
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 520–523
Burns R et al (2013) The Open Connectome Project Data Cluster: scalable analysis and vision for high-throughput neuroscience. SSDBM 2013
Chen H et al (2015) SmartTracing: self-learning based neuron reconstruction. Brain Informatics (submitted)
Collman F et al (2015) Mapping synapses by conjugate light-electron array tomography. J Neurosci 35:5792–5807
Danuser G (2011) Computer vision in cell biology. Cell 147:973–978
De Chaumont F et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696
Jenett A et al (2012) A GAL4-driver line resource for Drosophila neurobiology. Cell Rep 2:991–1001
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):1826–1831
Jug F et al (2014) Bioimage Informatics in the context of Drosophila research. Methods 68(1):60–73
Kasthuri N et al (2015) Saturated reconstruction of a volume of neocortex. Cell 162:648–661
Khmelinskii A et al (2012) Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nat Biotechnol 30:708–714
Kim J et al (2012) mGRASP enables mapping mammalian synaptic connectivity with light microscopy. Nat Methods 9:96–102
Kutsuna N et al (2012) Active learning framework with iterative clustering for bioimage classification. Nat Commun 3:1032
Kvilekval K et al (2010) Bisque: a platform for bioimage analysis and management. Bioinformatics 26:544–552
Li X et al (2015) Interactive exemplar-based segmentation toolkit for biomedical image analysis. International symposium on biomedical imaging: from nano to macro, pp 168–171
Long F et al (2009) A 3D digital atlas of C. elegans and its application to single-cell analyses. Nat Methods 6:667–672
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 9:305–315
Mancuso JJ et al (2013) Methods of dendritic spine detection: from Golgi to high-resolution optical imaging. Neuroscience 251:129–140
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):S26
Martone ME et al (2002) A cell-centered database for electron tomographic data. J Struct Biol 138:145–155
Micheva KD et al (2010) Single-synapse analysis of a diverse synapse population: proteomic imaging methods and markers. Neuron 68:639–653
Mikut R et al (2013) Automated processing of Zebrafish imaging data: a survey. Zebrafish 10(3):401–421
Myers G (2012) Why bioimage informatics matters. Nat Methods 9:659–660
Orlov N et al (2008) WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recognit Lett 29:1684–1693
Peng H (2008) Bioimage informatics: a new area of engineering biology. Bioinformatics 24:1827–1836
Peng H et al (2010) V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat Biotechnol 28:348–353
Peng H et al (2011) BrainAligner: 3D registration atlases of Drosophila brains. Nat Methods 8:493–498
Peng H et al (2012) Bioimage informatics: a new category in bioinformatics. Bioinformatics 28:1057
Peng H et al (2014a) Extensible visualization and analysis for multidimensional images using Vaa3D. Nat Protoc 9:193–208
Peng H et al (2014b) Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis. Nat Commun 5:4342
Peng H et al (2015a) BigNeuron: large-scale 3D neuron reconstruction from optical microscopy images. Neuron. doi:10.1016/j.neuron.2015.1006.1036
Peng H, Meijering E, Ascoli GA (2015b) From DIADEM to BigNeuron. Neuroinformatics 13:259–260
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 32:70–77
Saalfeld S et al (2009) CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25:1984–1986
Sanders J et al (2015) Learning-guided automatic three dimensional synapse quantification for drosophila neurons. BMC Bioinformatics 16:177
Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675
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:50610.53791/50696
Sommer C et al (2011) ilastik: interactive learning and segmentation toolkit. IEEE international symposium on biomedical imaging: from nano to macro, pp. 230–233
Swedlow JR et al (2003) Informatics and quantitative analysis in biological imaging. Science 300:100–102
Swedlow JR et al (2009) Bioimage informatics for experimental biology. Annu Rev Biophys 38:327–346
Tomer R et al (2012) Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy. Nat Methods 9:755–763
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 140046
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 399–403
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 324–332
Zhou J et al (2013b) BIOCAT: a pattern recognition platform for customizable biological image classification and annotation. BMC Bioinformatics 14:291. doi:210.1186/1471-2105-1114-1291
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Peng, H. et al. (2016). Bioimage Informatics for Big Data. In: De Vos, W., Munck, S., Timmermans, JP. (eds) Focus on Bio-Image Informatics. Advances in Anatomy, Embryology and Cell Biology, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-28549-8_10
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DOI: https://doi.org/10.1007/978-3-319-28549-8_10
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