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

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


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

See resource

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

See resource

Creation of generative models and synthetic images of cells

See resource

with our researchers

We are committed to bringing together multiple disciplines across science and engineering to address unmet medical needs.

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