eprintid: 28283
rev_number: 35
eprint_status: archive
userid: 5130
dir: disk0/00/02/82/83
datestamp: 2020-05-07 12:00:58
lastmod: 2020-06-15 12:56:47
status_changed: 2020-05-07 12:00:58
type: article
metadata_visibility: show
creators_name: Berg, Stuart
creators_name: Kutra, Dominik
creators_name: Kroeger, Thorben
creators_name: Straehle, Christoph N.
creators_name: Kausler, Bernhard X.
creators_name: Haubold, Carsten
creators_name: Schiegg, Martin
creators_name: Ales, Janez
creators_name: Beier, Thorsten
creators_name: Rudy, Markus
creators_name: Eren, Kemal
creators_name: Cervantes, Jaime I
creators_name: Xu, Buote
creators_name: Beuttenmueller, Fynn
creators_name: Wolny, Adrian
creators_name: Zhang, Chong
creators_name: Koethe, Ullrich
creators_name: Hamprecht, Fred A.
creators_name: Kreshuk, Anna
title: ilastik: interactive machine learning for (bio)image analysis
subjects: ddc-004
subjects: ddc-570
subjects: ddc-600
divisions: i-708000
divisions: i-708070
divisions: i-850800
keywords: deep learning in microscopy, machine learning, image analysis
cterms_swd: Deep learning
cterms_swd: Mikroskopie
cterms_swd: Maschinelles Lernen
cterms_swd: Bildanalyse
cterms_swd: Software
abstract: 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.
date: 2019-09
publisher: Nature Publishing Group
id_scheme: DOI
ppn_swb: 1698533136
own_urn: urn:nbn:de:bsz:16-heidok-282831
language: eng
bibsort: BERGSTUARTILASTIKINT201909
full_text_status: public
publication: Nature Methods
volume: 2019
number: 16
place_of_pub: London, New York
pagerange: 1-9
issn: 1548-7105 (Online-Ausg.), 1548-7091 (Druck-Ausg.)
citation:   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  (2019) ilastik: interactive machine learning for (bio)image analysis.  Nature Methods, 2019 (16).  pp. 1-9.  ISSN 1548-7105 (Online-Ausg.), 1548-7091 (Druck-Ausg.)     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/28283/7/Berg_ilastik_2020.pdf