Crossref journal-article
Springer Science and Business Media LLC
Nature Methods (297)
Bibliography

Berg, S., Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmueller, F., Wolny, A., Zhang, C., Koethe, U., Hamprecht, F. A., & Kreshuk, A. (2019). ilastik: interactive machine learning for (bio)image analysis. Nature Methods, 16(12), 1226–1232.

Authors 19
  1. Stuart Berg (first)
  2. Dominik Kutra (additional)
  3. Thorben Kroeger (additional)
  4. Christoph N. Straehle (additional)
  5. Bernhard X. Kausler (additional)
  6. Carsten Haubold (additional)
  7. Martin Schiegg (additional)
  8. Janez Ales (additional)
  9. Thorsten Beier (additional)
  10. Markus Rudy (additional)
  11. Kemal Eren (additional)
  12. Jaime I Cervantes (additional)
  13. Buote Xu (additional)
  14. Fynn Beuttenmueller (additional)
  15. Adrian Wolny (additional)
  16. Chong Zhang (additional)
  17. Ullrich Koethe (additional)
  18. Fred A. Hamprecht (additional)
  19. Anna Kreshuk (additional)
References 40 Referenced 2,778
  1. Simpson, R., Page, K. R. & De Roure, D. Zooniverse: observing the world’s largest citizen science platform. In Proc. 23rd International Conference on World Wide Web. 1049–1054 (ACM, 2014). (10.1145/2567948.2579215)
  2. Hughes, A. J. et al. Gartner. Quanti.us: a tool for rapid, flexible, crowd-based annotation of images. Nat. Methods 15, 587–590 (2018). (10.1038/s41592-018-0069-0) / Nat. Methods by AJ Hughes (2018)
  3. Sommer, C., Straehle, C., Köthe, U. & Hamprecht, F. A. ilastik: interactive learning and segmentation toolkit. In Proc. 8th IEEE International Symposium on Biomedical Imaging. 230–233 (IEEE, 2011). (10.1109/ISBI.2011.5872394)
  4. Erickson, B. J., Korfiatis, P., Akkus, Z. & Kline, T. L. Machine learning for medical imaging. RadioGraphics 37, 505–515 (2017). (10.1148/rg.2017160130) / RadioGraphics by BJ Erickson (2017)
  5. Geurts, P., Irrthum, A. & Wehenkel, L. Supervised learning with decision tree-based methods in computational and systems biology. Mol. BioSyst. 5, 1593–1605 (2009). (10.1039/b907946g) / Mol. BioSyst. by P Geurts (2009)
  6. Tarca, A. L., Carey, V. J., Chen, X., Romero, R. & Drăghici, S. Machine learning and its applications to biology. PLoS Comp. Biol. 3, 1–11 (2007). (10.1371/journal.pcbi.0030116) / PLoS Comp. Biol. by AL Tarca (2007)
  7. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001). (10.1023/A:1010933404324) / Mach. Learn. by L Breiman (2001)
  8. Fernández-Delgado, M., Cernadas, E., Barro, S. & Amorim, D. Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133–3181 (2014). / J. Mach. Learn. Res. by M Fernández-Delgado (2014)
  9. Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). / J. Mach. Learn. Res. by F Pedregosa (2011)
  10. Streichan, S. J., Hoerner, C. R., Schneidt, T., Holzer, D. & Hufnagel, L. Spatial constraints control cell proliferation in tissues. Proc. Natl Acad. Sci. USA 111, 5586–5591 (2014). (10.1073/pnas.1323016111) / Proc. Natl Acad. Sci. USA by SJ Streichan (2014)
  11. Schindelin, S. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012). (10.1038/nmeth.2019) / Nat. Methods by S Schindelin (2012)
  12. Tu, Z. & Bai, X. Auto-context and its application to high-level vision tasks and 3d brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intel. 32, 1744–1757 (2010). (10.1109/TPAMI.2009.186) / IEEE Trans. Pattern Anal. Mach. Intel. by Z Tu (2010)
  13. Raote, I. et al. Tango1 builds a machine for collagen export by recruiting and spatially organizing copii, tethers and membranes. eLife 7, e32723 (2018). (10.7554/eLife.32723) / eLife by I Raote (2018)
  14. Straehle, C. N., Köthe, U., Knott, G. W. & Hamprecht, F. A. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, (eds Fichtinger, G. et al.) 653–660 (Springer, 2011). (10.1007/978-3-642-23623-5_82)
  15. Straehle, C., Köthe, U., Briggman, K., Denk, W. & Hamprecht, F. A. Seeded watershed cut uncertainty estimators for guided interactive segmentation. Proc. CVPR 2012. 765–772 (CVPR, 2012). (10.1109/CVPR.2012.6247747)
  16. Maco, B. Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons. PLoS ONE 8, e57405 (2013). (10.1371/journal.pone.0057405) / PLoS ONE by B Maco (2013)
  17. Korogod, N., Petersen, C. C. & Knott, G. W. Ultrastructural analysis of adult mouse neocortex comparing aldehyde perfusion with cryo fixation. eLife 4, e05793 (2015). (10.7554/eLife.05793) / eLife by N Korogod (2015)
  18. Gonzalez-Tendero, A. et al. Whole heart detailed and quantitative anatomy, myofibre structure and vasculature from x-ray phase-contrast synchrotron radiation-based micro computed tomography. Cardiovas. Imag. 18, 732–741 (2017). / Cardiovas. Imag. by A Gonzalez-Tendero (2017)
  19. Jorstad, A., Blanc, J. & Knott, G. Neuromorph: a software toolset for 3d analysis of neurite morphology and connectivity. Front. Neuroanat. 12, 59 (2018). (10.3389/fnana.2018.00059) / Front. Neuroanat. by A Jorstad (2018)
  20. Nixon-Abell, J. et al. Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER. Science 354, 6311 (2016). (10.1126/science.aaf3928) / Science by J Nixon-Abell (2016)
  21. Stalling, D., Westerhoff, M. & Hege, H.-C. in The Visualization Handbook (eds Hansen, C. D. & Johnson, C. R.) Ch. 38, 749–767 (Elsevier, 2005). (10.1016/B978-012387582-2/50040-X)
  22. Andres, B., Kappes, J. H., Beier, T. B., Köthe, U. & Hamprecht, F. A. Probabilistic image segmentation with closedness constraints. In International Conference on Computer Vision. 2611–2618 (IEEE, 2011). (10.1109/ICCV.2011.6126550)
  23. Beier, T., Hamprecht, F. A. & Kappes, J. H. Fusion moves for correlation clustering. In IEEE Conference on Computer Vision and Pattern Recognition. 3507–3516 (IEEE, 2015). (10.1109/CVPR.2015.7298973)
  24. Beier, T. et al. Multicut brings automated neurite segmentation closer to human performance. Nat. Methods 14, 101–102 (2017). (10.1038/nmeth.4151) / Nat. Methods by T Beier (2017)
  25. Fiaschi, L., Koethe, U., Nair, R. & Hamprecht, F. A. Learning to count with regression forest and structured labels. In Proc. 21st International Conference on Pattern Recognition. 2685–2688 (IEEE, 2012).
  26. Schiegg, M., Hanslovsky, P., Kausler, B. X., Hufnagel, L. & Hamprecht, F. A. Conservation tracking. In 2013 IEEE International Conference on Computer Vision. 2928–2935 (IEEE, 2013). (10.1109/ICCV.2013.364)
  27. Haubold, C. et al. Segmenting and tracking multiple dividing targets using ilastik. In Focus on Bio-Image Informatics. 199–229 (Springer, 2016). (10.1007/978-3-319-28549-8_8)
  28. Lou, X. & Hamprecht, F. A. Structured learning from partial annotations. Proc. 29th International Conference on Machine Learning 1519–1526 (Omnipress, 2012).
  29. Haubold, C., Aleš, J., Wolf, S. & Hamprecht, F. A. in Computer Vision – ECCV 2016 (eds Leibe, B. et al.) 566–582 (Springer, 2016). (10.1007/978-3-319-46478-7_35)
  30. Wolff, C. et al. Multi-view light-sheet imaging and tracking with the mamut software reveals the cell lineage of a direct developing arthropod limb. eLife 7, e34410 (2018). (10.7554/eLife.34410) / eLife by C Wolff (2018)
  31. Berthold, M. R. et al. in Studies in Classification, Data Analysis, and Knowledge Organization (Gaul, W. et al.) 319–326 (Springer, 2007).
  32. Carpenter, A. E. et al. Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006). (10.1186/gb-2006-7-10-r100) / Genome Biol. by AE Carpenter (2006)
  33. Arganda-Carreras, I. et al. Trainable weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017). (10.1093/bioinformatics/btx180) / Bioinformatics by I Arganda-Carreras (2017)
  34. Sommer, C., Hoefler, R., Samwer, M. & Gerlich, D. W. A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol. Biol. Cell 28, 3428–3436 (2017). (10.1091/mbc.e17-05-0333) / Mol. Biol. Cell by C Sommer (2017)
  35. Luengo, I. et al. Survos: super-region volume segmentation workbench. J. Struct. Biol. 198, 43–53 (2017). (10.1016/j.jsb.2017.02.007) / J. Struct. Biol. by I Luengo (2017)
  36. Hilsenbeck, O. et al. faster: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy. Bioinformatics 33, 2020–2028 (2017). (10.1093/bioinformatics/btx107) / Bioinformatics by O Hilsenbeck (2017)
  37. Belevich, I., Joensuu, M., Kumar, D., Vihinen, H. & Jokitalo, E. Microscopy image browser: a platform for segmentation and analysis of multidimensional datasets. PLoS Biol. 14, 1–13 (2016). (10.1371/journal.pbio.1002340) / PLoS Biol. by I Belevich (2016)
  38. Marée, R. et al. Collaborative analysis of multi-gigapixel imaging data using cytomine. Bioinformatics 32, 1395–1401 (2016). (10.1093/bioinformatics/btw013) / Bioinformatics by R Marée (2016)
  39. Neumann, B. Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464, 721–727 (2010). (10.1038/nature08869) / Nature by B Neumann (2010)
  40. Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010). (10.1083/jcb.201004104) / J. Cell Biol. by M Linkert (2010)
Dates
Type When
Created 5 years, 10 months ago (Sept. 30, 2019, 3:47 p.m.)
Deposited 2 years, 3 months ago (May 20, 2023, 6:10 p.m.)
Indexed 10 minutes ago (Aug. 21, 2025, 1:38 a.m.)
Issued 5 years, 10 months ago (Sept. 30, 2019)
Published 5 years, 10 months ago (Sept. 30, 2019)
Published Online 5 years, 10 months ago (Sept. 30, 2019)
Published Print 5 years, 8 months ago (Dec. 1, 2019)
Funders 5
  1. Deutsche Forschungsgemeinschaft 10.13039/501100001659

    Region: Europe

    gov (National government)

    Labels3
    1. German Research Association
    2. German Research Foundation
    3. DFG
    Awards7
    1. HA 4364 9-1
    2. KR-4496/1-1
    3. CellNetworks
    4. SFB 1129
    5. HA 4364 10-1
    6. HA 4364 11-1
    7. FOR 2581
  2. HHMI Janelia Research Campus, Visiting Scientist Program
  3. Internal funding
  4. European Commission 10.13039/501100000780

    Region: Europe

    gov (National government)

    Labels26
    1. European Union
    2. Comisión Europea
    3. Europäische Kommission
    4. EU-Kommissionen
    5. Euroopa Komisjoni
    6. Ευρωπαϊκής Επιτροπής
    7. Европейската комисия
    8. Evropské komise
    9. Commission européenne
    10. Choimisiúin Eorpaigh
    11. Europskoj komisiji
    12. Commissione europea
    13. La Commissione europea
    14. Eiropas Komisiju
    15. Europos Komisijos
    16. Európai Bizottságról
    17. Europese Commissie
    18. Komisja Europejska
    19. Comissão Europeia
    20. Comisia Europeană
    21. Európskej komisii
    22. Evropski komisiji
    23. Euroopan komission
    24. Europeiska kommissionen
    25. EC
    26. EU
    Awards1
    1. HBP SGA1
  5. HHMI Janelia Research Campus Visiting Scientist Program

@article{Berg_2019, title={ilastik: interactive machine learning for (bio)image analysis}, volume={16}, ISSN={1548-7105}, url={http://dx.doi.org/10.1038/s41592-019-0582-9}, DOI={10.1038/s41592-019-0582-9}, number={12}, journal={Nature Methods}, publisher={Springer Science and Business Media LLC}, author={Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and Straehle, Christoph N. and Kausler, Bernhard X. and Haubold, Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I and Xu, Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong and Koethe, Ullrich and Hamprecht, Fred A. and Kreshuk, Anna}, year={2019}, month=sep, pages={1226–1232} }