ilastik: interactive machine learning for (bio) image analysis

被引：1655

作者：

Berg, Stuart ^{[1
]}

Kutra, Dominik ^{[2
,3
]}

Kroeger, Thorben ^{[2
]}

Straehle, Christoph N. ^{[2
]}

Kausler, Bernhard X. ^{[2
]}

Haubold, Carsten ^{[2
]}

Schiegg, Martin ^{[2
]}

Ales, Janez ^{[2
]}

Beier, Thorsten ^{[2
]}

Rudy, Markus ^{[2
]}

Eren, Kemal ^{[2
]}

Cervantes, Jaime I. ^{[2
]}

Xu, Buote ^{[2
]}

Beuttenmueller, Fynn ^{[2
,3
]}

Wolny, Adrian ^{[2
]}

Zhang, Chong ^{[2
]}

Koethe, Ullrich ^{[2
]}

Hamprecht, Fred A. ^{[2
]}

Kreshuk, Anna ^{[2
,3
]}

机构：

[1] HHMI Janelia Res Campus, Ashburn, VA USA

[2] Heidelberg Univ, HCI IWR, Heidelberg, Germany

[3] European Mol Biol Lab, Heidelberg, Germany

来源：

NATURE METHODS | 2019年 / 16卷 / 12期

关键词：

SEGMENTATION; TOOL;

D O I：

10.1038/s41592-019-0582-9

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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 non-linear 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.

引用

页码：1226 / 1232

页数：7

共 40 条

[1] Andres B, 2011, IEEE I CONF COMP VIS, P2611, DOI 10.1109/ICCV.2011.6126550
[2] Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
Arganda-Carreras, Ignacio
Kaynig, Verena
Rueden, Curtis
Eliceiri, Kevin W.
Schindelin, Johannes
Cardona, Albert
Seung, H. Sebastian
[J]. BIOINFORMATICS, 2017, 33 (15) : 2424 - 2426
[3] Multicut brings automated neurite segmenation closer to human performance
Beier, Thorsten
Pape, Constantin
Rahaman, Nasim
Prange, Limo
Berg, Stuart
Bock, Davi D.
Cardona, Albert
Knott, Graham W.
Plaza, Stephen M.
Scheffer, Louis K.
Koethe, Ullrich
Kreshuk, Anna
Hamprecht, Fred A.
[J]. NATURE METHODS, 2017, 14 (02) : 101 - 102
[4] Beier T, 2015, PROC CVPR IEEE, P3507, DOI 10.1109/CVPR.2015.7298973
[5] Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets
Belevich, Ilya
Joensuu, Merja
Kumar, Darshan
Vihinen, Helena
Jokitalo, Eija
[J]. PLOS BIOLOGY, 2016, 14 (01)
[6] KNIME:: The Konstanz Information Miner
Berthold, Michael R.
Cebron, Nicolas
Dill, Fabian
Gabriel, Thomas R.
Koetter, Tobias
Meinl, Thorsten
Ohl, Peter
Sieb, Christoph
Thiel, Kilian
Wiswedel, Bernd
[J]. DATA ANALYSIS, MACHINE LEARNING AND APPLICATIONS, 2008, : 319 - 326
[7] Random forests
Breiman, L
[J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
[8] CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, Anne E.
Jones, Thouis Ray
Lamprecht, Michael R.
Clarke, Colin
Kang, In Han
Friman, Ola
Guertin, David A.
Chang, Joo Han
Lindquist, Robert A.
Moffat, Jason
Golland, Polina
Sabatini, David M.
[J]. GENOME BIOLOGY, 2006, 7 (10)
[9] Machine Learning for Medical Imaging1
Erickson, Bradley J.
Korfiatis, Panagiotis
Akkus, Zeynettin
Kline, Timothy L.
[J]. RADIOGRAPHICS, 2017, 37 (02) : 505 - 515
[10] Fernández-Delgado M, 2014, J MACH LEARN RES, V15, P3133

← 1 2 3 4 →