Machine Learning: Advanced Image Segmentation Using ilastik

• DOI: 10.1007/978-1-4939-9686-5_21
• Publication Date: 2019-08-20T09:00:47Z
• Authors: Anna Kreshuk; Chong Zhang
• Keywords: 0301 basic medicine; 0206 medical engineering; Datasets as Topic; 02 engineering and technology; Somatosensory Cortex; Mitochondria; Workflow; Machine Learning; 03 medical and health sciences; Mice; Microscopy, Electron; Image Processing, Computer-Assisted; Animals; Software

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.