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Master Machine-Learning-Based Image Analysis with ilastik: Overview and Tutorial | Dr. Dominik Kutra

Welcome to the LIBRE_hub Seminars Master Machine-Learning-Based Image Analysis with ilastik: A Overview and Tutorial By Dr. Dominik Kutra from March 15, 2023 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. Related Publications 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/s41592-019-05... 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.2021.0.... 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/s41592-021-01... ‌ Related Links: https://www.ilastik.org/ LIBRE_hub seminar series bring and connect international speakers with the Latin American bioimaging community through a wide range of topics related to bioimaging technologies. These seminars are part of the LIBRE_hub training program for open-source bioimaging hardware. On our channel you will also find other seminars on relevant topics. Feel free to subscribe to the channel for more free seminars. What does LIBRE_hub stand for? The Latin American Hub for Bioimaging Through Open Hardware (LIBRE_hub) is a training network for open source bioimaging hardware in Latin America with the goal to empower regional researchers through practical workshops, seminars, networking, and online resources adapted to local needs and re-published in local languages. We invite you to subscribe to our newsletter, be part of our growing community and stay informed about the main news we have in our project. https://mailchi.mp/2efa11be3d6b/libre... More information about us: https://librehub.github.io/