Quantitative Image Analysis Hub

The aim of the CTI QIA Hub is to provide training, support, and networking opportunities for researchers working on any aspect of quantitative image analysis. Our goal is to bring together investigators working at every scale, from nano to macro, and every level of experience, from beginner to expert. By bridging diverse fields, applications, and approaches to computational image analysis, the QIA Hub will help foster scientific excellence and research innovation across the university.

The QIA Hub will serve as a first port of call for links to software, tutorials, and other resources for image analysis and data science, including statistics and machine learning. In addition to external resources, the QIA Hub will host internal instructional sessions, webinars, and user forums for specific software and applications. It will also provide a platform for sharing information - such as analysis pipelines and experimental protocols - and building collaborations across disciplines.

Image analysis software:

FIJI/ImageJ - workhorse image processing

General image processing workhorse, runs open-source plugins and macros for all manner of applications (pre-processing, segmentation, measurement, tracking)

See resource

Ilastik

Machine learning annotation-based segmentation and tracking tool, works with time series and z-stacks

CellProfiler

Cell segmentation and measurement, also has worm toolbox, large online user support community

See resource

QuPath

Machine learning annotation-based tool for region and cell identification and measurement in histology sections

See resource

CellOrganizer

Creation of generative models and synthetic images of cells

See resource

A Picture Is Worth A Thousand Datapoints: High-Content Image Analysis For Cellular Studies

This lecture discusses the fundamentals of using computational methods to extract information from microscopy images, from basic thresholding to segmentation algorithms to deep learning. Examples of how these methods are being used in biomedical research and medicine are surveyed.

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Extracting Metadata from file names - the magic of Regex for CellProfiler

This video explains how to use Regex - Regular Expressions - to pull out information from file names and, specifically, how to use Regex and metadata to load files into CellProfiler. Regex can also be used in ImageJ/FIJI and other programs. Knowing Regex will also help you think about how to systematize your file names to make them easier to use and organize in general. This explainer is a by a biologist, for computer newbies - no coding experience necessary.

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Measuring Nuclear/Cytoplasmic Intensity with CellProfiler

Detailed walk-through of a CellProfiler pipeline to quantify nuclear localization of a protein from microscope images. I discuss the pros and cons of using the whole cytoplasm versus the perinuclear ring region and the importance of log transforming the ratio. Labels used here were Hoechst (DNA and nuclear segmentation) and anti-YAP/TAZ (subcellular localization and cell segmentation).

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