Abstract
Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demonstrate two workflows: Pixel Classification and Autocontext. We illustrate their use on a challenging problem in electron microscopy image segmentation. After following this walk-through, we expect the readers to be able to apply the necessary steps to their own data and segment their images by either workflow.
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Kreshuk, A., Zhang, C. (2019). Machine Learning: Advanced Image Segmentation Using ilastik. In: Rebollo, E., Bosch, M. (eds) Computer Optimized Microscopy. Methods in Molecular Biology, vol 2040. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9686-5_21
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DOI: https://doi.org/10.1007/978-1-4939-9686-5_21
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