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  • Automatic approaches for microscopy

    imaging based on machine learning

    and spatial statistics

    Ramin Norousi

    Munchen 2013

  • Automatic approaches for microscopy

    imaging based on machine learning

    and spatial statistics

    Ramin Norousi

    Dissertation

    an der Fakultat fur Mathematik, Informatik und Statistik

    der LudwigMaximiliansUniversitat

    Munchen

    vorgelegt von

    Ramin Norousi

    aus Teheran

    Heidelberg, den 06.11.2013

  • Erstgutachter: Prof. Dr. Volker Schmid, LMU Munchen

    Zweitgutachter: Prof. Dr. Achim Tresch, Universitat zu KolnDrittgutachter: Prof. Dr. Christian Heumann, LMU Munchen

    Tag der Disputation: 07. Februar 2014

  • Contents

    Scope of this Work XI

    I Particle Picking in 3D Cryo-EM XIII

    1. Introduction 1

    1.1 Cryo-Electron Microscopy (Cryo-EM) . . . . . . . . . . . . . . . . . . 11.2 Process of 3D Electron Microscopy (3DEM) . . . . . . . . . . . . . . 31.3 Challenges in 3DEM . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Our Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

    2. Theory Basics 9

    2.1 Particle Picking Methods in 3DEM process . . . . . . . . . . . . . . . 92.2 Principles of Machine Learning . . . . . . . . . . . . . . . . . . . . . 13

    2.2.1 Supervised Learning Methods . . . . . . . . . . . . . . . . . . 152.2.2 Classification Model Assessment . . . . . . . . . . . . . . . . . 202.2.3 Classification Ensemble . . . . . . . . . . . . . . . . . . . . . . 272.2.4 Challenges in Machine Learning . . . . . . . . . . . . . . . . . 29

    3. Material and Methods 31

    3.1 Workflow of MAPPOS . . . . . . . . . . . . . . . . . . . . . . . . . . 313.1.1 Construction of a Training Set . . . . . . . . . . . . . . . . . . 333.1.2 Determining of Discriminatory Features . . . . . . . . . . . . 333.1.3 Construction of an Classifier Ensemble . . . . . . . . . . . . . 36

    3.2 Validation of MAPPOS . . . . . . . . . . . . . . . . . . . . . . . . . 373.2.1 Validation based on Artificial Data . . . . . . . . . . . . . . . 383.2.2 Validation based on real Cryo-EM Data . . . . . . . . . . . . 40

    I

  • CONTENTS

    4. Experiments and Results 434.1 Performance in a Simulated Data Environment . . . . . . . . . . . . . 434.2 Performance with Simulated Cryo-EM Data . . . . . . . . . . . . . . 444.3 Performance of MAPPOS vs. Human Experts . . . . . . . . . . . . . 46

    5. Conclusion of Part I 53

    II Spot Detection and Colocalization Analysis in 3D Mul-tichannel Fluorescent Imagesbased on Spatial Statistics 55

    6. Introduction 576.1 Fluorescence Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . 586.2 Colocalization Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 596.3 Challenges of Colocalization in Fluorescence Microscopy . . . . . . . 606.4 Our Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

    7. Theory Basics 677.1 Image Acquisition Techniques . . . . . . . . . . . . . . . . . . . . . . 67

    7.1.1 Light Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . 687.1.2 Confocal Laser Scanning Microscopy (CLSM) . . . . . . . . . 697.1.3 Structured Illumination Microscopy (3D-SIM) . . . . . . . . . 717.1.4 Principles of Digital Imaging . . . . . . . . . . . . . . . . . . . 72

    7.2 Spot Detection and Quantification in Fluorescent Images . . . . . . . 767.2.1 Manual Detection and Quantification . . . . . . . . . . . . . . 767.2.2 Automatic Detection and Quantification Methods . . . . . . . 77

    7.3 Colocalization Measuring Methods . . . . . . . . . . . . . . . . . . . 807.3.1 Intensity Correlation Coefficient-Based (ICCB) . . . . . . . . 817.3.2 Object-based Approach . . . . . . . . . . . . . . . . . . . . . . 84

    7.4 Colocalization Analysis based on Spatial Point Processes . . . . . . . 877.4.1 Introduction to Spatial Point Processes . . . . . . . . . . . . . 887.4.2 Point Process Distributions . . . . . . . . . . . . . . . . . . . 917.4.3 Spatial Statistic Approaches . . . . . . . . . . . . . . . . . . . 977.4.4 Monte Carlo Test using Envelopes . . . . . . . . . . . . . . . . 111

    8. Material and Methods 1138.1 Workflow of 3D-OSCOS . . . . . . . . . . . . . . . . . . . . . . . . . 113

    8.1.1 Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . 115

    II

  • CONTENTS

    8.1.2 Image Preprocessing . . . . . . . . . . . . . . . . . . . . . . . 1178.1.3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1228.1.4 Colocalization Analysis . . . . . . . . . . . . . . . . . . . . . . 1278.1.5 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . 128

    8.2 Input and Output of 3D-OSCOS . . . . . . . . . . . . . . . . . . . . 1298.2.1 User Interactions and Inputs . . . . . . . . . . . . . . . . . . . 1298.2.2 Program Outputs . . . . . . . . . . . . . . . . . . . . . . . . . 130

    9. Experiments and Results 1359.1 Validation of 3D-OSCOS . . . . . . . . . . . . . . . . . . . . . . . . 135

    9.1.1 Validation based on Real Data set . . . . . . . . . . . . . . . . 1369.1.2 Validation based on Artificial Data set . . . . . . . . . . . . . 136

    9.2 Performance measurements . . . . . . . . . . . . . . . . . . . . . . . . 1399.2.1 Performance based on Artificial Data set . . . . . . . . . . . . 1399.2.2 Performance based on Real Data set . . . . . . . . . . . . . . 143

    10. Conclusion of Part II 147

    11. Discussion 149

    Appendix I 155

    Appendix II 163

    Abbreviations 169

    III

  • Abstract

    One of the most frequent ways to interact with the surrounding environment occursas a visual way. Hence imaging is a very common way in order to gain informationand learn from the environment. Particularly in the field of cellular biology, imagingis applied in order to get an insight into the minute world of cellular complexes. Asa result, in recent years many researches have focused on developing new suitableimage processing approaches which have facilitates the extraction of meaningfulquantitative information from image data sets. In spite of recent progress, but due tothe huge data set of acquired images and the demand for increasing precision, digitalimage processing and statistical analysis are gaining more and more importance inthis field.

    There are still limitations in bioimaging techniques that are preventing sophi-sticated optical methods from reaching their full potential. For instance, in the 3DElectron Microscopy(3DEM) process nearly all acquired images require manual post-processing to enhance the performance, which should be substitute by an automaticand reliable approach (dealt in Part I). Furthermore, the algorithms to localize in-dividual fluorophores in 3D super-resolution microscopy data are still in their initialphase (discussed in Part II). In general, biologists currently lack automated and highthroughput methods for quantitative global analysis of 3D gene structures.

    This thesis focuses mainly on microscopy imaging approaches based on MachineLearning, statistical analysis and image processing in order to cope and improve thetask of quantitative analysis of huge image data. The main task consists of buildinga novel paradigm for microscopy imaging processes which is able to work in anautomatic, accurate and reliable way.

    V

  • Abstract

    The specific contributions of this thesis can be summarized as follows:

    Substitution of the time-consuming, subjective and laborious task of manualpost-picking in Cryo-EM process by a fully automatic particle post-pickingroutine based on Machine Learning methods (Part I).

    Quality enhancement of the 3D reconstruction image due to the high perfor-mance of automatically post-picking steps (Part I).

    Developing a full automatic tool for detecting subcellular objects in multichan-nel 3D Fluorescence images (Part II).

    Extension of known colocalization analysis by using spatial statistics in orderto investigate the surrounding point distribution and enabling to analyze thecolocalization in combination with statistical significance (Part II).

    All introduced approaches are implemented and provided as toolboxes which arefree available for research purposes.

    VI

  • Zusammenfassung

    Einer der haufigsten Wege, mit dem Umfeld zu interagieren ist die visuelle Inter-aktion. Daher zahlen die bildgebende Verfahren zu den sehr verbreiteten Ansatzenfur die Informationsgewinnung und demzufolge das Lernen aus dem Umfeld. Spe-ziell im Bereich der Zellkernbiologie werden bildgebende Verfahren eingesetzt, umEinblicke in die winzige Welt der Zellen zu verschaffen. Demzufolge haben sich vie-le Forschungsprojekte mit der Entwicklung von geeigneten Ansatzen fur die Bild-verarbeitung beschaftig. Diese Ansatze sollen dazu dienen, die Extraktion von be-deutungsvollen quantitativen Daten aus den Bildern zu ermoglichen. Aufgrund dergroen Datenmenge der anfallenden Bilder und des Bedarfs an einer moglichst ob-jektiven Untersuchung mit hoher Genauigkeit, haben die digitale Bildvearbeitungund die statistische Analyse viel an Bedeutung gewonnen.

    Es gibt immer noch Einschrankungen bzgl. der Bio-Imaging Techniken, die unsdaran hindern eine noch anspruchsvollere Methode fur die Gewinnung der gesam-ten potenziellen Information zu erlangen. Beispielsweise im Bereich der 3D-ElectronMikroskopie benotigen die aufgenommenen Bilder eine manuelle Nachbearbeitungum die Performanz und das Ergebnis zu optimieren. Dieser Schritt sollte anhandeiner automatischen Routine ersetzt werden (Teil I). Des Weiteren befindet sichder Algorithmus fur die Lokalisierung von fluoreszierende Proteine in hochauflosen-den mikroskopischen 3D-Bilder in ihren Anfangen (Teil II). Im Allgemeinen fehltes den Biologen gegenwartig geeigne