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Biological Image Analysis Made Easy

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If you have a question about this talk, please contact Florian Markowetz.

Note the unusual time: 11am not 4pm!

Biology has become a data-driven science. Much of the raw data now comes in the form of images, creating a need for easy-to-use tools for automated quantitative analysis.

In this talk, I will describe and demo our current best attempt at realizing such a tool: ilastik ( is a convenient tool for image classification and segmentation which requires no experience in image processing.

The interactive training of a powerful classifier allows to distinguish an arbitrary number of classes (such as different tissue, different organelles, etc) provided that these are distinguishable by local appearance. The program provides real-time feedback of the current classifier predictions and thus allows for targeted training and overall reduced labeling time. Once the classifier has been trained on a representative subset of the data, it can be used to automatically process a very large number of images. ilastik works on gray value, color or spectral images with up to three spatial dimensions.

I will demo selected applications from high-throughput screening, the neurosciences and mass spectrometric imaging, and be around to experiment with images supplied by the audience.

ilastik is open source and available from

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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