ilastik: interactive machine learning for (bio)image analysis

By S. Berg, D. Kutra, Thorben Kroeger, Christoph Nikolas Straehle, Bernhard X. Kausler, Carsten Haubold, Martin Schiegg, J. Ales, Thorsten Beier, M. Rudy and 9 more. 2019

2,939 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Molecular Biology, Medical Imaging, Human-Computer Interaction, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Elena Hernández-Pereira
2. Eduardo Mosqueira-Rey
3. Á. Fernández-Leal
4. José Bobes-Bascarán
5. David Alonso-Ríos
6. Oliver Stegle
7. Anne E Carpenter
8. Florian Jug

In Biophysics

1. C. Stringer
2. Allen Goodman
3. Roser Vento-Tormo
4. Robert Haase
5. Oliver Stegle
6. Deborah Schmidt
7. Anne E Carpenter
8. Florian Jug

In Molecular Biology

1. Allen Goodman
2. Roser Vento-Tormo
3. Oliver Stegle
4. Anne E Carpenter
5. Florian Jug
6. E. Dann
7. C. Stringer
8. Moritz Gerstung

In Medical Imaging

1. C. Stringer
2. Anne E Carpenter

In Computational Biology & Bioinformatics

1. Oliver Stegle
2. Anne E Carpenter

Citations in the corpus

• Human-in-the-loop machine learning: a state of the art (2022, methodology): “Among the recent developments within this technique we can name the ilastik tool (Berg et al. 2019) that contains pre-defined workflows for image segmentation, object classification, counting and tracking and that allows non-expert users to interactively provide annotations to steep the learning…”
• Cell2location maps fine-grained cell types in spatial transcriptomics (2022, methodology): “The segmentation of astrocytes was performed on the Agt image channel with pixel classification of ilastik 61 .”
• Fast and sensitive GCaMP calcium indicators for imaging neural populations (2021, neutral): “05) and comparable to 8m (84[32-165] ms) and XCaMP-Gf (91[48-155] ms) (Fig.”
• Labkit: Labeling and Segmentation Toolkit for Big Image Data (2021, methodology): “In LABKIT, the random forest classifier is trained on manually labeled pixels (scribbles), an approach similar to ilastik (Berg et al., 2019) or Trainable Weka Segmentation (Arganda-Carreras et al., 2017).”
• CellProfiler 4: improvements in speed, utility and usability (2021, methodology): “Numerous smaller packages are tooled towards specific types of data: for example, QuPath is a popular program geared specifically towards pathology applications [2], while Ilastik delivers an interactive machine learning framework to assist users in segmenting images [3].”
• Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation (2021, background): “Ilastik is a popular interactive machine-426 learning tool for bioimage segmentation, but training it using a manual interface was not feasible on a large and diverse dataset such as our own (21).”