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A Short Course on Bioimage Informatics Lecture 1: Basic Concepts and Techniques

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A Short Course on Bioimage Informatics Lecture 1: Basic Concepts and Techniques A Short Course on Bioimage Informatics Lecture 1: Basic Concepts and Techniques Ge Yang (杨戈) Department of Biomedical Engineering & Department of Computational Biology Carnegie Mellon University August 10, 2015 1 Acknowledgements • Dr. Chao Tang • Dr. Luhua Lai • Students, faculty, and staff of the Center for Quantitative Biology, Peking University • Dr. Jing Yuan (袁静) • Youjun Xu (徐优俊) • 111 Visiting Scholar Program, Ministry of Education • College of Chemistry and Molecular Engineering, Peking University • School of Engineering & School of Computer Science, Carnegie Mellon University 2 Outline • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of bioimage informatics techniques • Summary 3 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 4 What information can we extract from images? 刘奇 5 More Examples of Biological Images Typical Readouts from Images • From static images, e.g. immunofluorescence images - Location - Shape/morphology - Spatial distribution: e.g. spatial patterns - Spatial relation: e.g. protein colocalization • From dynamic images, e.g. live cell time-lapse images - All the readouts above, which now may be time-dependent - In other words, spatiotemporal dynamics/processes • Computational analysis techniques are required to extract these quantitative readouts from images. 7 Relations of Imaging and Image Analysis with Other Biological Research Tools • None of these readouts can be directly provided by e.g. genetics or biochemistry. • However, understanding these readouts often requires close integration of computational image analysis with experimental analysis using genetics and/or biochemistry techniques. Alberts, MBoC, 5/e, 2007 8 Some Practical Questions • Q1: If I have an image analysis problem to solve, where do I look for information and solutions? • Q2: What techniques are available? Which technique should I choose? How can I be sure that I am getting the correct results? • Q3: How can I go from image analysis results to molecular mechanisms? • We will address these questions in this course. 9 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 10 What is Bioimage Informatics? • To give a simple definition, bioimage informatics is the computational analysis and understanding of biological images. • Bioimage informatics emerged as a new technical area primarily over the past ten years. - First meeting held in 2005 at Stanford University. https://sites.google.com/site/hanchuanpeng/bioimageinformatics_resource 11 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 12 Looking into History: Robert Hooke’s Microscope • Microscope was invented more than 300 years ago. 1) http://micro.magnet.fsu.edu/index.html Molecular expressions: microscopy world 2) http://www.history-of-the-microscope.org/ Robert Hooke (1635-1703) 13 Evolution of Microscopes http://www.microscopyu.com/museum/index.html 14 Pioneering Work of Computational Analysis of Biological Images • Shinya Inoue pioneered the use of video recording devices and digital image processing in microscopy in late 1970s to early 1980s. • It was found that digital image processing can improve image quality. • Today, we use the term “computational image analysis” interchangeably with “digital image processing”. Inoue, S. 1981. Video image processing greatly enhances contrast, quality Shinya Inoue, and speed in polarization based microscopy. J. Cell Biol. 89:346–356. Marine Biology Lab, Woods Hole, MA 15 Development of Image Processing and Computer Vision Techniques • Early work in digital image processing started in 1960s. • Computer vision techniques started as an area of robotics and artificial intelligence around the same period of time. • The work of Shinya Inoue is another example of interdisciplinary science. 16 Automation of Image Collection http://micro.magnet.fsu.edu/index.html Molecular expressions: microscopy world 17 High-Throughput Microscopy Pepperkok & Ellenberg, 2006 Multi-well plates 18 Advances in Fluorescence Microscopy Livet J, Weissman TA, Kang H, et al. Nature 450: 56–62, 2007 http://www.fda.gov/forconsumers/consumerupdates/ucm107783.htm 19 Systems Level Study of Biological Processes June 26, 2000 • International Human Genome Sequencing Consortium (2001). "Initial sequencing and analysis of the human genome" Nature 409 (6822): 860–921. • Venter, JC et al. (2001). "The sequence of the human genome" . Science 291 (5507): 1304– 1351. 20 Advances in Computer Vision Takeo Kanade lab, CMU 21 Advances in Medical Image Analysis Heimann et al, Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets, IEEE Trans. Medical Imaging, vol. 28, no. 8, 1251-1265, 2009. 22 Summary • Development of bioimage informatics techniques is driven by the synergy of several factors: - The need for quantitative and systems-level study biological processes - Availability of computational image analysis techniques - Availability of automated image collection - Interdisciplinary collaboration •Q: If I have an image analysis problem to solve, where do I look for information and solutions? A: Many techniques have been developed specifically for biological image analysis. In addition, areas such as computer vision, medical image analysis are also important sources of techniques. 23 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 24 Important Trends in Fluorescence Microscopy (I): Fluorescence Biosensors Rho GTPase sensor in mouse embryonic fibroblast, Machacek et al, Nature, 2009 • Spatiotemporal cell dynamics visualized by fluorescence biosensors. Important Trends in Fluorescence Microscopy (II): Super-Resolution Microscopy Chen et al, IEEE Int. Sym. Biomed Imaging, 2014 26 Important Trends in Fluorescence Microscopy (III): Light-Sheet Microscopy B.-C. Chen et al, Science, 346: 439, 2014. 27 Summary • Bioimage informatics techniques are required for understanding complex spatiotemporal dynamics revealed by fluorescence biosensors. • Bioimage informatics techniques are required for further improve the resolution of super-resolution microscopy techniques. • Light sheet microscopy will drive the transition of cellular imaging from 2D to 3D. Bioimage informatics techniques are essential for 3D image analysis. 28 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 29 General Comments • To analyze biological images, it is often necessary to have at least some knowledge about the image formation process. • It is often feasible to significantly simplify image analysis by optimizing the imaging process. collaboration between image collection and image analysis • It is critical to choose imaging settings properly. Erroneous setting can make the collected images unusable. Nyquist/Shannon sampling criterion 30 Spectrum of Visible Light Color Wavelength violet 380–450 nm blue 450–495 nm green 495–570 nm yellow 570–590 nm orange 590–620 nm red 620–750 nm http://en.wikipedia.org/wiki/Visible_spectrum http://science.hq.nasa.gov/kids/imagers/ems/visible.html 31 Microscope as a Linear System • A light microscope can be considered as a linear system. http://micro.magnet.fsu.edu/primer/java/imageformation/airydiskformation/index.html • A linear system is characterized by its impulse response. For a microscope, this is its point spread function (PSF). 32 Airy Disk • Airy (after George Biddell Airy) disk is the diffraction pattern of a point feature under a circular aperture. • It has the following form 2 2Jx1 y x J1(x) is a Bessel function of the first kind. • Detailed derivation is given in Born & Wolf, Principles of Optics, 7th ed., pp. 439-441. 33 Microscope Image Formation • Microscope image formation can be modeled as a convolution with the PSF. I x,y O x,y psf x,y F I x,y F O x,y F psf x,y http://micro.magnet.fsu.edu/primer/java/mtf/airydisksize/index.html 34 Resolution Limit of Light Microscopy 061. • Rayleigh limit D NA • Sparrow limit 047. D NA http://www.microscopy.fsu.edu/primer/java/imageformation/rayleighdisks/index.html 35 Nyquist/Shannon Sampling Theorem • The Nyquist-Shannon sampling theorem: The sampling frequency should be at least twice the highest frequency contained in the signal. • This is true for both spatial and temporal sampling. • Spatial sampling is determined by the effective pixel size. It should be no larger than one half of the smallest feature size. • Temporal sampling is determined by frame rate. It should be at least twice as fast as the dynamic biological process to be visualized. 36 • Background • What is bioimage informatics? • Where does it come from? • Where is it going? • Basic concepts of image formation and collection • Overview of biological image analysis techniques • Summary 37 From Biological Images to Biological Knowledge 38 How are image analysis problem
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