Master Machine-Learning-Based Image Analysis with ilastik

Master Machine-Learning-Based Image Analysis with ilastik: Overview and Tutorial

Overview

ilastik is an easy-to-use interactive open-source tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains predefined workflows for image segmentation, object classification, counting and tracking. An overview of ilastik will be given, demonstrating how to apply it through various examples and how to integrate it with other image analysis tools

Speaker

Dr. Dominik Kutra, Software Developer at EMBL Heidelberg, special interest in making machine learning methods accessible.

Adrian Wolny, Lorenzo Cerrone, Athul Vijayan, Rachele Tofanelli, Amaya Vilches Barro, Marion Louveaux, Christian Wenzl, Sören Strauss, David Wilson-Sánchez, Rena Lymbouridou, Susanne S Steigleder, Constantin Pape, Alberto Bailoni, Salva Duran-Nebreda, George W Bassel, Jan U Lohmann, Miltos Tsiantis, Fred A Hamprecht, Kay Schneitz, Alexis Maizel, Anna Kreshuk (2020) Accurate and versatile 3D segmentation of plant tissues at cellular resolution eLife 9:e57613 https://doi.org/10.7554/eLife.57613

Berg, S., Kutra, D., Kroeger, T. et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods 16, 1226–1232 (2019). https://doi.org/10.1038/s41592-019-0582-9

Hernando M. Vergara, Constantin Pape, Kimberly I. Meechan, Valentyna Zinchenko, Christel Genoud, Adrian A. Wanner, Kevin Nzumbi Mutemi, Benjamin Titze, Rachel M. Templin, Paola Y. Bertucci, Oleg Simakov, Wiebke Dürichen, Pedro Machado, Emily L. Savage, Lothar Schermelleh, Yannick Schwab, Rainer W. Friedrich, Anna Kreshuk, Christian Tischer, Detlev Arendt. Whole-body integration of gene expression and single-cell morphology. Cell, Volume 184, Issue 18, 2021, Pages 4819-4837.e22, ISSN 0092-8674, https://doi.org/10.1016/j.cell.2021.07.017.

Musser JM, Schippers KJ, Nickel M, Mizzon G, Kohn AB, Pape C, Ronchi P, Papadopoulos N, Tarashansky AJ, Hammel JU, Wolf F, Liang C, Hernández-Plaza A, Cantalapiedra CP, Achim K, Schieber NL, Pan L, Ruperti F, Francis WR, Vargas S, Kling S, Renkert M, Polikarpov M, Bourenkov G, Feuda R, Gaspar I, Burkhardt P, Wang B, Bork P, Beck M, Schneider TR, Kreshuk A, Wörheide G, Huerta-Cepas J, Schwab Y, Moroz LL, Arendt D. Profiling cellular diversity in sponges informs animal cell type and nervous system evolution. Science. 2021 Nov 5;374(6568):717-723. doi: 10.1126/science.abj2949. Epub 2021 Nov 4. PMID: 34735222; PMCID: PMC9233960.

Wagner, N., Beuttenmueller, F., Norlin, N. et al. Deep learning-enhanced light-field imaging with continuous validation. Nat Methods 18, 557–563 (2021). https://doi.org/10.1038/s41592-021-01136-0 ‌

Repositories

ilastik - Website

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