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1. Introduction to BigDIPA [Fowlkes]

A brief overview and introduction of the course topics and our motivations for teaching it.

2. Lecture: High-data Rate Optical Microscopy [Digman, Gratton]

This lecture provides an overview of available high data-rate microscopy systems, basic physical constraints on microscope designs, and approaches for optimizing configurations for the collection of image volumes with high spatial and temporal resolution. Operating principles of modern fluorescence microscopes including optical sectioning, confocal microscopy and cutting edge developments in instrumentation including Selective Plane Illumination (SPIM) and super resolution microscopy.

3. Software Install and Discussion [Fowlkes]

Students will be introduced to software tools and compute resources that will be used in the rest of the course. Hands on time during which students will install software, log in to accounts, access shared data resources etc. Students will complete some warm up exercises to prepare them for lab sections in subsequent days. Students will discuss what imaging and software tools they are currently using in their own research and why.

4. Lecture: Fluorescence Correlation Microscopy [Gratton, Digman]

Single molecular detection is critical for measuring diffusion, binding kinetics and aggregation state of proteins. In a microscope setup, fluorescence fluctuations of tagged proteins are detected in a small focal volume (~10-15L). Detection in a very small volume increases molecular number fluctuations and it allows correlation and cross-correlation of single molecular fluctuations using the autocorrelation function of the fluorescence intensity. These lectures will provide fundamental understanding of the physical properties of molecular motion, flows and transport. It will also specifically cover the fluorescence Diffusion Tensor Imaging (fDTI) analysis. This lecture will describe the basic mathematical and computational tools needed to understand correlation analysis in one, two and three dimensions.

5. Lab: Fluorescence Correlation measurement and Analysis [Gratton, Digman]

Lecture will describe case study of processing big image data produced by very fast acquisition of FCS data and need to optimize the downstream processing. Narrative discussion of engineering development of the software (FCS Globals), basic approach/software libraries used to make it fast, resulting performance speedup, etc. Students will tour LFD and opportunity to collect data with uSPIM instrument. Students will perform hands on computational correlation analysis of FCS datasets.

6. Applications of Fluorescence Correlation Microscopy [Digman]

Live single cell spatio-temporal analysis of protein dynamics provides a mean to observe stochastic biochemical signaling which may lead to better understanding of cancer cell invasion, stem cell differentiation and other fundamental biological processes. This talk will describe an application of the pCF (pair correlation function) analysis to understand p53 activity as an example of protein dynamic interactions in living cells. p53 is a tumor suppressor protein that regulates target genes involved in DNA damage migration and repair. If cells become stressed due to DNA damage, p53 will form tetramers at specific chromatin sites and will activate gene transcription of specific proteins that trigger cell cycle arrest or apoptosis. To gain information regarding fast dynamic processes of p53 behavior, we have imaged p53 with the single plane illumination microscope (SPIM) suing a fast sCMOS camera as a function of time to map different modes of diffusion: confined, directed and Brownian. These analyses can provide answers to the following biological questions: how fast are proteins interacting in their local environment and at what spatial scale?

7. Lecture: Image Processing [Fowlkes]

This will provide general mathematical background on processing of digital images. Students will learn basic principles of sampling and linear filtering with application examples of detecting edges and oriented structures. Theory will provide a context for understanding common image processing methods for biological imaging including deconvolution, image warping and registering and blending multiple volumes acquired during tiled acquisition of large volumes.

8. Lecture: Detection, Segmentation and Tracking [Fowlkes]

To extract meaningful high-level descriptions of image content such as 3D location and extent of cells requires processing the output of trained classifiers evaluated over the image to produce discrete lists of points (e.g. cell centers), lines (e.g. traced axons) and planes (e.g. membrane surfaces). This lecture will introduce the basic processing of local image features to identify such geometric structures across a range of scales (from intracellular to whole organisms). Discussion of how these methods scale up to very large datasets.

9. Guest Lecture: Quantitative Image Analysis for Studying Stem-cell Cycling in the C. elegans Gonad [Olivier Cinquin, UC Irvine]

Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. I will discuss a technique that relies on such spatial cytometry to characterize cell cycling, and an application to a well-established stem cell model system: the C. elegans gonad. This application has allowed us to investigate the relation between cell cycling and reproductive senescence.

10. Lecture: Machine Learning for Image Analysis [Xie]

Modern computer vision methods make heavy use of algorithms that automatically determine useful image features and tune algorithm parameters to maximize accuracy. This lecture will introduce basic formulations of classification and regression prediction tasks and architectures for performing these tasks based on linear prediction, decision trees and “deep” artificial neural networks. Students will gain a high-level understanding of how these architectures are automatically optimized from training examples and how image features can be selected that provide predictive power. Students will see several case-studies that use these techniques for specific big image data applications and will be introduced to several software packages that implement these methods and make them accessible to end users who lack programming expertise.

11. Lab: Recognizing the Patterns of Major Subcellular Structures in Fluorescently labeled Cells [Xie]

Students will have hands on practice with using a variety of image processing software and learn strategies for quantifying accuracy of these and diagnosing potential problems in their application to data.

12. Guest Lecture: Labeling and Imaging Neurons in Whole Intact Brains [Viviana Gradinaru, Caltech]

Since Ramón y Cajal’s first illustrations of neuronal structures, neuroscientists have benefited from new ways to visualize the microscopic components within the brain. The development of advanced imaging techniques holds the promise for elucidating brain works. This lecture will describe recently developed techniques that allow for the transformation of intact tissue into a hydrogel-hybridized form that is fully intact but optically transparent and macromolecule permeable.

13. Lab: Neuron Tracing Lab [Fowlkes]

Neuron cellular morphology plays a huge role in the function of neurons as information processing units. This lab will explore methods for processing images to produce quantitative descriptions of neuron morphology and meso-scale connectivity.

14. Lecture: High-performance Computing for Image Analysis [Fowlkes]

Distributing computation over clusters of commodity computers has been a mainstay of scientific computing since the mid 90s, particularly in computationally intensive engineering and physics simulations. In contrast, infrastructure for data intensive computation (relatively few calculations carried out repeatedly on extremely large quantities of data) is undergoing rapid development made possible by falling storage prices (that allow data to be replicated near compute nodes) and robust, open software platforms backed by industry interest in large-scale data analytics. In this lecture students will be introduced to background on cluster computing, principles of distributing computations, and practical guidelines for running jobs on compute clusters and multi-core architectures.

15. Lab: High-performance Analysis of Fluorescence Correlation [Gohlke]

Scaling up image processing to very large datasets involves using a variety of techniques to optimize the processing of individual images, splitting computational tasks across multiple processors and optimizing the transfer of image data from storage to memory to cpu. This lab will explore the optimization of a relatively simple correlation calculation in order to speed it up by orders of magnitude and allow interactive visualization of results.

16. Lecture: Spatial and Graph Data Analysis [Smyth]

Low-level quantitative image analysis produces noisy local estimates of, e.g. molecular concentrations, diffusion tensors, etc. regularly sampled over an image volume. Such data can be analyzed in terms of models that assume (for example) the existence of locally smooth flows or spatially varying densities. In this lecture students will be introduced to basic statistical tools that are appropriate for modeling such spatial processes from regularly sampled or sparse measurements. Students will also be introduced to ways in which connectivity data can be represented by networks and graphs and how to extract useful insights from relatively complex graphs, characterizing different aspects of the connectivity, including cluster structure, scale, etc. This lecture will cover the basic statistical modeling assumptions and computations needed to fit such models to data.

17. Lab: Spatial and Graph Data Analysis [Smyth]

Students will experiment with computing spatial statistics of structures extracted from image data and perform graph-based analysis of connectivity data.

18. Lecture: Big Data Visualization [Gopi]

The capability to interactively visualize very large image data and spatially structured analysis results is widely acknowledged as essential to scientific understanding. Visualization techniques in general are evaluated based on their ability to convey the pattern and characteristics of the data in a truthful and easy to perceive manner. This lecture will first discuss good and bad visualizations of data through examples, using this criterion. Then different visualization techniques for different data types will be discussed along with concrete examples. Further, the concept of color and contrast which are important aspects of visualization will be explained. Given these fundamentals of visualization, we will elaborate on visualizing volumetric data and data types derived from imaging data (tensors, vector fields, etc.). Participants will also engage in discussion of scientific ethics associated with data visualization (i.e. tweaking contrast in paper figures, choosing a colormap that hides noise, etc.).

19. Lab: Data Visualization [Gopi]

Students will use general purpose visualization tools and some simple scripting to explore different visualization techniques.

20. Lecture: Scalable Cloud Computing and Image Databases [Schwartz]

This lecture and lab will provide an overview of the costs, benefits and challenges of using cloud computing infrastructure to perform big data image analysis and hands-on experience with the ViQi / Bisque image analysis platform.

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