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ilastik: interactive machine learning for (bio)image analysis

Berg, Stuart ; Kutra, Dominik ; Kroeger, Thorben ; Straehle, Christoph N. ; Kausler, Bernhard X. ; Haubold, Carsten ; Schiegg, Martin ; Ales, Janez ; Beier, Thorsten ; Rudy, Markus ; Eren, Kemal ; Cervantes, Jaime I ; Xu, Buote ; Beuttenmueller, Fynn ; Wolny, Adrian ; Zhang, Chong ; Koethe, Ullrich ; Hamprecht, Fred A. ; Kreshuk, Anna

In: Nature Methods, 2019 (September 2019), Nr. 16. pp. 1-9. ISSN 1548-7105 (Online-Ausg.), 1548-7091 (Druck-Ausg.)

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We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

Item Type: Article
Journal or Publication Title: Nature Methods
Volume: 2019
Number: 16
Publisher: Nature Publishing Group
Place of Publication: London, New York
Date Deposited: 07 May 2020 12:00
Date: September 2019
ISSN: 1548-7105 (Online-Ausg.), 1548-7091 (Druck-Ausg.)
Page Range: pp. 1-9
Faculties / Institutes: Service facilities > Interdisciplinary Center for Scientific Computing
Service facilities > Heidelberg Collaboratory for Image Processing (HCI)
Service facilities > European Molecular Biology Laboratory (EMBL)
Subjects: 004 Data processing Computer science
570 Life sciences
600 Technology (Applied sciences)
Controlled Keywords: Deep learning, Mikroskopie, Maschinelles Lernen, Bildanalyse, Software
Uncontrolled Keywords: deep learning in microscopy, machine learning, image analysis
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