Leger, L.-A., Leonardi, M., Salati, A., Naef, F., & Weigert, M. (2025). Sequence models for continuous cell cycle stage prediction from brightfield images. ArXiv Preprint ArXiv:2502.02182.
@article{leger2025sequence,
title = {Sequence models for continuous cell cycle stage prediction from brightfield images},
author = {Leger, Louis-Alexandre and Leonardi, Maxine and Salati, Andrea and Naef, Felix and Weigert, Martin},
journal = {arXiv preprint arXiv:2502.02182},
year = {2025},
doi = {10.48550/arXiv.2502.02182}
}
Müller, A., Schmidt, D., Albrecht, J. P., Rieckert, L., Otto, M., Galicia Garcia, L. E., Fabig, G., Solimena, M., & Weigert, M. (2024). Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets. Nature Protocols, 1–31.
@article{muller2024,
title = {Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets},
author = {M{\"u}ller, Andreas and Schmidt, Deborah and Albrecht, Jan Philipp and Rieckert, Lucas and Otto, Maximilian and Galicia Garcia, Leticia Elizabeth and Fabig, Gunar and Solimena, Michele and Weigert, Martin},
journal = {Nature Protocols},
pages = {1--31},
year = {2024},
publisher = {Nature Publishing Group UK London},
doi = {10.1038/s41596-024-00957-5}
}
Graham, S., Vu, Q. D., Jahanifar, M., Weigert, M., Schmidt, U., Zhang, W., Zhang, J., Yang, S., Xiang, J., Wang, X., & others. (2024). CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting. Medical Image Analysis, 92, 103047.
@article{graham2024conic,
title = {CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting},
author = {Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Weigert, Martin and Schmidt, Uwe and Zhang, Wenhua and Zhang, Jun and Yang, Sen and Xiang, Jinxi and Wang, Xiyue and others},
journal = {Medical image analysis},
volume = {92},
pages = {103047},
year = {2024},
publisher = {Elsevier},
doi = {10.1016/j.media.2024.103047}
}
Gallusser, B., & Weigert, M. (2024). Trackastra: Transformer-based cell tracking for live-cell microscopy. European Conference on Computer Vision, 467–484.
@inproceedings{gallusser2024trackastra,
title = {Trackastra: Transformer-based cell tracking for live-cell microscopy},
author = {Gallusser, Benjamin and Weigert, Martin},
booktitle = {European Conference on Computer Vision},
pages = {467--484},
year = {2024},
organization = {Springer},
doi = {10.1007/978-3-031-73116-7_27}
}
Gwerder, M., Demir, C. S., Williams, H. L., Lugli, A., Martinez, C. G., Kowal, J., Khan, A., Kirchner, P., Koessler, T., Berger, M. D., & others. (2024). Morpho-molecular features of Epithelial Mesenchymal Transition associate with clinical outcome in patients with rectal cancer. BioRxiv, 2024–2011.
@article{gwerder2025,
title = {Morpho-molecular features of Epithelial Mesenchymal Transition associate with clinical outcome in patients with rectal cancer},
author = {Gwerder, Mauro and Demir, Cansaran Saygili and Williams, Hannah L and Lugli, Alessandro and Martinez, Cristina Graham and Kowal, Joanna and Khan, Amjad and Kirchner, Philipp and Koessler, Thibaud and Berger, Martin D and others},
journal = {bioRxiv},
pages = {2024--11},
year = {2024},
publisher = {Cold Spring Harbor Laboratory},
doi = {10.1101/2024.11.07.622481}
}
Skoufa, E., Zhong, J., Kahre, O., Hu, K., Tsissios, G., Carrau, L., Herrera, A., Dominguez Mantes, A., Castilla-Ibeas, A., Jang, H., Weigert, M., Manno, G. L., Lutolf, M., Ros, M., & Aztekin, C. (2024). Specialized signaling centers direct cell fate and spatial organization in a limb organoid model. BioRxiv. https://www.biorxiv.org/content/early/2024/07/03/2024.07.02.601324
@article{skoufa2025,
author = {Skoufa, Evangelia and Zhong, Jixing and Kahre, Oliver and Hu, Kelly and Tsissios, Georgios and Carrau, Louise and Herrera, Antonio and Dominguez Mantes, Albert and Castilla-Ibeas, Alejandro and Jang, Hwanseok and Weigert, Martin and Manno, Gioele La and Lutolf, Matthias and Ros, Marian and Aztekin, Can},
title = {Specialized signaling centers direct cell fate and spatial organization in a limb organoid model},
elocation-id = {2024.07.02.601324},
year = {2024},
doi = {10.1101/2024.07.02.601324},
publisher = {Cold Spring Harbor Laboratory},
url = {https://www.biorxiv.org/content/early/2024/07/03/2024.07.02.601324},
eprint = {https://www.biorxiv.org/content/early/2024/07/03/2024.07.02.601324.full.pdf},
journal = {bioRxiv}
}
Specialized signaling centers orchestrate robust development and regeneration. Limb morphogenesis, for instance, requires interactions between the mesoderm and the signaling center apical-ectodermal ridge (AER), whose properties and role in cell fate decisions have remained challenging to dissect. To tackle this, we developed mouse embryonic stem cells (mESCs)-based heterogeneous cultures and a limb organoid model, termed budoids, comprising cells with AER, surface ectoderm, and mesoderm properties. mESCs were first induced into heterogeneous cultures that self-organized into domes in 2D. Aggregating these cultures resulted in formation of limb bud-like structures in 3D, exhibiting chondrogenesis-based symmetry breaking and elongation. Using our organoids and quantitative in situ expression profiling, we uncovered that AER-like cells support nearby limb mesoderm and fibroblast identities while enhancing tissue polarization that permits distant cartilage formation. Together, our findings provide a powerful model to study aspects of limb morphogenesis, and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.Competing Interest StatementM.L. is an employee of F. Hoffman-La Roche. The other authors declare no competing interests.
Sarkis, R., Burri, O., Royer-Chardon, C., Schyrr, F., Blum, S., Costanza, M., Cherix, S., Piazzon, N., Barcena, C., Bisig, B., Nardi, V., Sarro, R., Ambrosini, G., Weigert, M., Spertini, O., Blum, S., Deplancke, B., Seitz, A., de Leval, L., & Naveiras, O. (2023). MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in experimental and clinical hematology. Modern Pathology, 100088. https://www.sciencedirect.com/science/article/pii/S0893395222055284
@article{sarkis2023,
title = {MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in experimental and clinical hematology},
journal = {Modern Pathology},
pages = {100088},
year = {2023},
issn = {0893-3952},
doi = {https://doi.org/10.1016/j.modpat.2022.100088},
url = {https://www.sciencedirect.com/science/article/pii/S0893395222055284},
author = {Sarkis, Rita and Burri, Olivier and Royer-Chardon, Claire and Schyrr, Frédérica and Blum, Sophie and Costanza, Mariangela and Cherix, Stephane and Piazzon, Nathalie and Barcena, Carmen and Bisig, Bettina and Nardi, Valentina and Sarro, Rossella and Ambrosini, Giovanna and Weigert, Martin and Spertini, Olivier and Blum, Sabine and Deplancke, Bart and Seitz, Arne and {de Leval}, Laurence and Naveiras, Olaia},
keywords = {open-source, cellularity, adiposity, stroma, hematopathology, digital pathology, bone marrow}
}
Bürgy, L., Weigert, M., Hatzopoulos, G., Minder, M., Journé, A., Rahi, S. J., & Gönczy, P. (2023). CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets. BMC Bioinformatics, 24(1), 1–10.
@article{burgy2023,
title = {CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets},
author = {B{\"u}rgy, L{\'e}o and Weigert, Martin and Hatzopoulos, Georgios and Minder, Matthias and Journ{\'e}, Adrien and Rahi, Sahand Jamal and G{\"o}nczy, Pierre},
journal = {BMC Bioinformatics},
volume = {24},
number = {1},
pages = {1--10},
year = {2023},
publisher = {BioMed Central},
doi = {10.1186/s12859-023-05214-2}
}
Gallusser, B., Stieber, M., & Weigert, M. (2023). Self-supervised dense representation learning for live-cell microscopy with time arrow prediction. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).
@article{gallusser2023,
title = {Self-supervised dense representation learning for live-cell microscopy with time arrow prediction},
author = {Gallusser, Benjamin and Stieber, Max and Weigert, Martin},
journal = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
year = {2023},
doi = {10.48550/arXiv.2305.05511}
}
Rocha-Martins, M., Nerli, E., Kretzschmar, J., Weigert, M., Icha, J., Myers, E. W., & Norden, C. (2023). Neuronal migration prevents spatial competition in retinal morphogenesis. Nature, 1–10.
@article{rocha2023,
title = {Neuronal migration prevents spatial competition in retinal morphogenesis},
author = {Rocha-Martins, Mauricio and Nerli, Elisa and Kretzschmar, Jenny and Weigert, Martin and Icha, Jaroslav and Myers, Eugene W and Norden, Caren},
journal = {Nature},
pages = {1--10},
year = {2023},
publisher = {Nature Publishing Group UK London},
doi = {10.1038/s41586-023-06392-y}
}
Eisenstein, M. (2023). AI under the microscope: the algorithms powering the search for cells. Nature, 623(7989), 1095–1097.
@article{eisenstein2023,
title = {AI under the microscope: the algorithms powering the search for cells.},
author = {Eisenstein, Michael},
journal = {Nature},
volume = {623},
number = {7989},
pages = {1095--1097},
year = {2023},
doi = {10.1038/d41586-023-03722-y}
}
Mahecic, D., Stepp, W. L., Zhang, C., Griffié, J., Weigert, M., & Manley, S. (2022). Event-driven acquisition for content-enriched microscopy. Nature Methods, 19(10), 1262–1267. https://doi.org/10.1038/s41592-022-01589-x
@article{mahecic2022,
title = {Event-driven acquisition for content-enriched microscopy},
volume = {19},
issn = {1548-7105},
url = {https://doi.org/10.1038/s41592-022-01589-x},
doi = {10.1038/s41592-022-01589-x},
pages = {1262--1267},
number = {10},
journaltitle = {Nature Methods},
shortjournal = {Nature Methods},
author = {Mahecic, Dora and Stepp, Willi L. and Zhang, Chen and Griffié, Juliette and Weigert, Martin and Manley, Suliana},
year = {2022}
}
A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.
Ouyang, W., Beuttenmueller, F., Gómez-de-Mariscal, E., Pape, C., Burke, T., Garcia-López-de-Haro, C., Russell, C., Moya-Sans Lucı́a, de-la-Torre-Gutiérrez, C., Schmidt, D., Kutra, D., Novikov, M., Weigert, M., Schmidt, U., Bankhead, P., Jacquemet, G., Sage, D., Henriques, R., Muñoz-Barrutia, A., … Kreshuk, A. (2022). BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis. BioRxiv. https://www.biorxiv.org/content/early/2022/06/08/2022.06.07.495102
@article{ouyang2022,
author = {Ouyang, Wei and Beuttenmueller, Fynn and G{\'o}mez-de-Mariscal, Estibaliz and Pape, Constantin and Burke, Tom and Garcia-L{\'o}pez-de-Haro, Carlos and Russell, Craig and Moya-Sans, Luc{\'\i}a and de-la-Torre-Guti{\'e}rrez, Cristina and Schmidt, Deborah and Kutra, Dominik and Novikov, Maksim and Weigert, Martin and Schmidt, Uwe and Bankhead, Peter and Jacquemet, Guillaume and Sage, Daniel and Henriques, Ricardo and Mu{\~n}oz-Barrutia, Arrate and Lundberg, Emma and Jug, Florian and Kreshuk, Anna},
title = {BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis},
elocation-id = {2022.06.07.495102},
year = {2022},
doi = {10.1101/2022.06.07.495102},
publisher = {Cold Spring Harbor Laboratory},
url = {https://www.biorxiv.org/content/early/2022/06/08/2022.06.07.495102},
journal = {bioRxiv}
}
Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a model’s overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.Competing Interest StatementThe authors have declared no competing interest.
Weigert, M., & Schmidt, U. (2022). Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist. 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), 1–4.
@inproceedings{weigert2022,
author = {Weigert, Martin and Schmidt, Uwe},
booktitle = {2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC)},
title = {Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist},
year = {2022},
volume = {},
number = {},
pages = {1-4},
doi = {10.1109/ISBIC56247.2022.9854534}
}
Weigert, M., & Schmidt, U. (2022). Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist. (ISBIC), 1–4.
@inproceedings{weigert2022_short,
author = {Weigert, Martin and Schmidt, Uwe},
booktitle = {(ISBIC)},
title = {Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist},
year = {2022},
volume = {},
number = {},
pages = {1-4},
doi = {10.1109/ISBIC56247.2022.9854534}
}
Gallusser, B., Maltese, G., Di Caprio, G., Vadakkan, T. J., Sanyal, A., Somerville, E., Sahasrabudhe, M., O’Connor, J., Weigert, M., & Kirchhausen, T. (2022). Deep neural network automated segmentation of cellular structures in volume electron microscopy. Journal of Cell Biology, 222(2).
@article{gallusser2022,
title = {Deep neural network automated segmentation of cellular structures in volume electron microscopy},
author = {Gallusser, Benjamin and Maltese, Giorgio and Di Caprio, Giuseppe and Vadakkan, Tegy John and Sanyal, Anwesha and Somerville, Elliott and Sahasrabudhe, Mihir and O'Connor, Justin and Weigert, Martin and Kirchhausen, Tom},
journal = {Journal of Cell Biology},
volume = {222},
number = {2},
year = {2022},
publisher = {The Rockefeller University Press},
doi = {10.1083/jcb.202208005}
}
Müller, A., Schmidt, D., Xu, C. S., Pang, S., D’Costa, J. V., Kretschmar, S., Münster, C., Kurth, T., Jug, F., Weigert, M., Hess, H. F., & Michele, S. (2021). 3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse βcells. Journal of Cell Biology, 220(2).
@article{mueller2021,
title = {3D FIB-SEM reconstruction of microtubule--organelle interaction in whole primary mouse $\beta$ cells},
author = {M{\"u}ller, Andreas and Schmidt, Deborah and Xu, C Shan and Pang, Song and D’Costa, Joyson Verner and Kretschmar, Susanne and M{\"u}nster, Carla and Kurth, Thomas and Jug, Florian and Weigert, Martin and Hess, Harald F and Michele, Solimena*},
journal = {Journal of Cell Biology},
volume = {220},
number = {2},
year = {2021},
publisher = {The Rockefeller University Press},
doi = {10.1083/jcb.202010039}
}
Müller, A., Schmidt, D., Xu, C. S., Pang, S., D’Costa, J. V., Kretschmar, S., Münster, C., Kurth, T., Jug, F., Weigert, M., Hess, H. F., & Michele, S. (2021). 3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse βcells. JCB.
@article{mueller2021_short,
title = {3D FIB-SEM reconstruction of microtubule--organelle interaction in whole primary mouse $\beta$ cells},
author = {M{\"u}ller, Andreas and Schmidt, Deborah and Xu, C Shan and Pang, Song and D’Costa, Joyson Verner and Kretschmar, Susanne and M{\"u}nster, Carla and Kurth, Thomas and Jug, Florian and Weigert, Martin and Hess, Harald F and Michele, Solimena*},
journal = {JCB},
year = {2021},
publisher = {The Rockefeller University Press},
doi = {10.1083/jcb.202010039}
}
Juppet, Q., De Martino, F., Marcandalli, E., Weigert, M., Burri, O., Unser, M., Brisken, C., & Sage, D. (2021). Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features. Journal of Mammary Gland Biology and Neoplasia .
@article{juppet2021,
title = {Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features},
author = {Juppet, Quentin and De Martino, Fabio and Marcandalli, Elodie and Weigert, Martin and Burri, Olivier and Unser, Michael and Brisken, Cathrin and Sage, Daniel},
journal = {Journal of Mammary Gland Biology and Neoplasia },
year = {2021},
publisher = {Springer},
doi = {10.1007/s10911-021-09485-4}
}
Wagner, N., Beuttenmueller, F., Norlin, N., Gierten, J., Boffi, J. C., Wittbrodt, J., Weigert, M., Hufnagel, L., Prevedel, R., & Kreshuk, A. (2021). Deep learning-enhanced light-field imaging with continuous validation. Nature Methods, 18(5), 557–563.
@article{wagner2021,
title = {Deep learning-enhanced light-field imaging with continuous validation},
author = {Wagner, Nils and Beuttenmueller, Fynn and Norlin, Nils and Gierten, Jakob and Boffi, Juan Carlos and Wittbrodt, Joachim and Weigert, Martin and Hufnagel, Lars and Prevedel, Robert and Kreshuk, Anna},
journal = {Nature Methods},
volume = {18},
number = {5},
pages = {557--563},
year = {2021},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-021-01136-0}
}
Wang, Y., Eddison, M., Fleishman, G., Weigert, M., Xu, S., Wang, T., Rokicki, K., Goina, C., Henry, F. E., Lemire, A. L., & others. (2021). EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization. Cell, 184(26), 6361–6377.
@article{wang2021,
title = {EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization},
author = {Wang, Yuhan and Eddison, Mark and Fleishman, Greg and Weigert, Martin and Xu, Shengjin and Wang, Tim and Rokicki, Konrad and Goina, Cristian and Henry, Fredrick E and Lemire, Andrew L and others},
journal = {Cell},
volume = {184},
number = {26},
pages = {6361--6377},
year = {2021},
publisher = {Elsevier},
doi = {10.1016/j.cell.2021.11.024}
}
Eisenstein, M. (2020). Smart solutions for automated imaging. Nature Methods, 17(11), 1075–1079.
@article{nm_eisenstein2020smart,
title = {Smart solutions for automated imaging},
author = {Eisenstein, Michael},
journal = {Nature Methods},
volume = {17},
number = {11},
pages = {1075--1079},
year = {2020},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-020-00988-2}
}
Haase, R., Royer, L. A., Steinbach, P., Schmidt, D., Dibrov, A., Schmidt, U., Weigert, M., Maghelli, N., Tomancak, P., Jug, F., & Myers, E. W. (2020). CLIJ: GPU-accelerated image processing for everyone. Nature Methods, 17(1), 5–6.
@article{haase2020,
title = {CLIJ: GPU-accelerated image processing for everyone},
author = {Haase, Robert and Royer, Loic A and Steinbach, Peter and Schmidt, Deborah and Dibrov, Alexandr and Schmidt, Uwe and Weigert, Martin and Maghelli, Nicola and Tomancak, Pavel and Jug, Florian and Myers, Eugene W.},
journal = {Nature Methods},
volume = {17},
number = {1},
pages = {5--6},
year = {2020},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-019-0650-1}
}
Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. IEEE Winter Conference on Applications of Computer Vision (WACV).
@inproceedings{weigert2020,
author = {Weigert, Martin and Schmidt, Uwe and Haase, Robert and Sugawara, Ko and Myers, Gene},
title = {Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy},
booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
year = {2020},
doi = {10.1109/WACV45572.2020.9093435}
}
Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. WACV.
@inproceedings{weigert2020_short,
author = {Weigert, Martin and Schmidt, Uwe and Haase, Robert and Sugawara, Ko and Myers, Gene},
title = {Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy},
booktitle = {WACV},
year = {2020},
doi = {10.1109/WACV45572.2020.9093435}
}
Broaddus, C., Krull, A., Weigert, M., Schmidt, U., & Myers, G. (2020). Removing Structured Noise with Self-Supervised Blind-Spot Networks. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 159–163.
@inproceedings{broaddus2020,
title = {Removing Structured Noise with Self-Supervised Blind-Spot Networks},
author = {Broaddus, Coleman and Krull, Alexander and Weigert, Martin and Schmidt, Uwe and Myers, Gene},
booktitle = {2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
pages = {159--163},
year = {2020},
organization = {IEEE},
doi = {10.1109/ISBI45749.2020.9098336}
}
Schmell, S., Zakrzewski, F., de Back, W., Weigert, M., Schmidt, U., Wenke, T., Zeugner, S., Mantey, R., Sperling, C., Roeder, I., Hoenscheid, P., Aust, D., & Baretton, G. (2020). An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics. Medical Imaging with Deep Learning (MIDL).
@inproceedings{schmell2020,
title = {An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics},
author = {Schmell, Sarah and Zakrzewski, Falk and de Back, Walter and Weigert, Martin and Schmidt, Uwe and Wenke, Torsten and Zeugner, Silke and Mantey, Robert and Sperling, Christian and Roeder, Ingo and Hoenscheid, Pia and Aust, Daniela and Baretton, Gustavo},
booktitle = {Medical Imaging with Deep Learning (MIDL)},
year = {2020},
doi = {10.48550/arXiv.2005.12066}
}
Dietler, N., Minder, M., Gligorovski, V., Economou, A. M., Joly, D. A. H. L., Sadeghi, A., Chan, C. H. M., Koziński, M., Weigert, M., Bitbol, A.-F., & Rahi, S. J. (2020). A convolutional neural network segments yeast microscopy images with high accuracy. Nature Communications, 11(1), 5723. https://doi.org/10.1038/s41467-020-19557-4
@article{dietler2020,
author = {Dietler, Nicola and Minder, Matthias and Gligorovski, Vojislav and Economou, Augoustina Maria and Joly, Denis Alain Henri Lucien and Sadeghi, Ahmad and Chan, Chun Hei Michael and Kozi{\'n}ski, Mateusz and Weigert, Martin and Bitbol, Anne-Florence and Rahi, Sahand Jamal},
da = {2020/11/12},
date-added = {2020-11-17 15:27:54 +0100},
date-modified = {2020-11-17 15:27:54 +0100},
doi = {10.1038/s41467-020-19557-4},
id = {Dietler2020},
isbn = {2041-1723},
journal = {Nature Communications},
number = {1},
pages = {5723},
title = {A convolutional neural network segments yeast microscopy images with high accuracy},
ty = {JOUR},
url = {https://doi.org/10.1038/s41467-020-19557-4},
volume = {11},
year = {2020}
}
The identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually.
Saha, D., Schmidt, U., Zhang, Q., Barbotin, A., Hu, Q., Ji, N., Booth, M. J., Weigert, M., & Myers, E. W. (2020). Practical sensorless aberration estimation for 3D microscopy with deep learning. Optics Express, 28(20), 29044–29053.
@article{saha2020,
title = {Practical sensorless aberration estimation for 3D microscopy with deep learning},
author = {Saha, Debayan and Schmidt, Uwe and Zhang, Qinrong and Barbotin, Aurelien and Hu, Qi and Ji, Na and Booth, Martin J and Weigert, Martin and Myers, Eugene W},
journal = {Optics Express},
volume = {28},
number = {20},
pages = {29044--29053},
year = {2020},
publisher = {Optical Society of America},
doi = {10.1364/OE.401933}
}
Strack, R. (2019). Deep learning in imaging. Nature Methods, 16(1), 17–17.
@article{nm_strack2019deep,
title = {Deep learning in imaging},
author = {Strack, Rita},
journal = {Nature methods},
volume = {16},
number = {1},
pages = {17--17},
year = {2019},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-018-0267-9}
}
Zakrzewski, F., de Back, W., Weigert, M., Wenke, T., Zeugner, S., Mantey, R., Sperling, C., Friedrich, K., Roeder, I., Aust, D., Baretton, G., & Hönscheid, P. (2019). Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues. Scientific Reports, 9(1), 8231.
@article{zakrzewski2019,
author = {Zakrzewski, Falk and de Back, Walter and Weigert, Martin and Wenke, Torsten and Zeugner, Silke and Mantey, Robert and Sperling, Christian and Friedrich, Katrin and Roeder, Ingo and Aust, Daniela and Baretton, Gustavo and H{\"o}nscheid, Pia},
doi = {10.1038/s41598-019-44643-z},
isbn = {2045-2322},
journal = {Scientific Reports},
number = {1},
pages = {8231},
title = {Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues},
volume = {9},
year = {2019}
}
The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal, low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17). By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.
Subramanian, K., Weigert, M., Borsch, O., Petzold, H., Garcia-Ulloa, A., Myers, E. W., Ader, M., Solovei, I., & Kreysing, M. (2019). Rod nuclear architecture determines contrast transmission of the retina and behavioral sensitivity in mice. ELife, 8, e49542.
@article{subramanian2019,
title = {Rod nuclear architecture determines contrast transmission of the retina and behavioral sensitivity in mice},
author = {Subramanian, Kaushikaram and Weigert, Martin and Borsch, Oliver and Petzold, Heike and Garcia-Ulloa, Alfonso and Myers, Eugene W and Ader, Marius and Solovei, Irina and Kreysing, Moritz},
journal = {eLife},
volume = {8},
pages = {e49542},
year = {2019},
publisher = {eLife Sciences Publications Limited},
doi = {10.7554/eLife.49542}
}
Weigert, M., Subramanian, K., Bundschuh, S. T., Myers, E. W., & Kreysing, M. (2018). Biobeam – Multiplexed wave-optical simulations of light-sheet microscopy. PLoS Computational Biology.
@article{weigert2018a,
author = {Weigert, Martin and Subramanian, Kaushikaram and Bundschuh, Sebastian T. and Myers, Eugene W. and Kreysing, Moritz},
title = {Biobeam -- Multiplexed wave-optical simulations of light-sheet microscopy},
journal = {PLoS Computational Biology},
year = {2018},
doi = {10.1371/journal.pcbi.1006079}
}
Weigert, M., Subramanian, K., Bundschuh, S. T., Myers, E. W., & Kreysing, M. (2018). Biobeam – Multiplexed wave-optical simulations of light-sheet microscopy. PLoS Comp. Bio.
@article{weigert2018a_short,
author = {Weigert, Martin and Subramanian, Kaushikaram and Bundschuh, Sebastian T. and Myers, Eugene W. and Kreysing, Moritz},
title = {Biobeam -- Multiplexed wave-optical simulations of light-sheet microscopy},
journal = {PLoS Comp. Bio.},
year = {2018},
doi = {10.1371/journal.pcbi.1006079}
}
Vogt, N. (2018). Simulating the imaging process in scattering tissue. Nature Methods, 15(6), 406–406.
@article{nm_vogt2018biobeam,
title = {Simulating the imaging process in scattering tissue},
author = {Vogt, Nina},
journal = {Nature methods},
volume = {15},
number = {6},
pages = {406--406},
year = {2018},
publisher = {Nature Publishing Group},
doi = {10.1038/s41592-018-0031-1}
}
Weigert, M., Schmidt, U., Boothe, T., Müller, A., Dibrov, A., Jain, A., Wilhelm, B., Schmidt, D., Broaddus, C., Culley, S., Rocha-Martins, M., Segovia-Miranda, F., Norden, C., Henriques, R., Zerial, M., Solimena, M., Rink, J., Tomancak, P., Royer, L., … Myers, E. W. (2018). Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy. Nature Methods, 15(12), 1090–1097. https://doi.org/10.1038/s41592-018-0216-7
@article{weigert2018b,
author = {Weigert, Martin and Schmidt, Uwe and Boothe, Tobias and M{\"u}ller, Andreas and Dibrov, Alexandr and Jain, Akanksha and Wilhelm, Benjamin and Schmidt, Deborah and Broaddus, Coleman and Culley, Si{\^a}n and Rocha-Martins, Mauricio and Segovia-Miranda, Fabi{\'a}n and Norden, Caren and Henriques, Ricardo and Zerial, Marino and Solimena, Michele and Rink, Jochen and Tomancak, Pavel and Royer, Loic and Jug, Florian and Myers, Eugene W.},
doi = {10.1038/s41592-018-0216-7},
journal = {Nature Methods},
number = {12},
pages = {1090--1097},
title = {Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy},
ty = {JOUR},
url = {https://doi.org/10.1038/s41592-018-0216-7},
volume = {15},
year = {2018}
}
Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to tenfold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times-higher frame rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME.
Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018). Cell Detection with Star-Convex Polygons. Medical Image Computing and Computer Assisted Intervention - MICCAI, 265–273.
@article{schmidt2018,
author = {Schmidt, Uwe and Weigert, Martin and Broaddus, Coleman and Myers, Gene},
title = {Cell Detection with Star-Convex Polygons},
booktitle = {Medical Image Computing and Computer Assisted Intervention - {MICCAI}},
pages = {265--273},
year = {2018},
doi = {10.1007/978-3-030-00934-2_30}
}
Sui, L., Alt, S., Weigert, M., Dye, N., Eaton, S., Jug, F., Myers, E. W., Jülicher, F., Salbreux, G., & Dahmann, C. (2018). Differential lateral and basal tension drive folding of Drosophila wing discs through two distinct mechanisms. Nature Communications, 9(1), 4620. https://doi.org/10.1038/s41467-018-06497-3
@article{sui2018,
author = {Sui, Liyuan and Alt, Silvanus and Weigert, Martin and Dye, Natalie and Eaton, Suzanne and Jug, Florian and Myers, Eugene W. and J{\"u}licher, Frank and Salbreux, Guillaume and Dahmann, Christian},
doi = {10.1038/s41467-018-06497-3},
isbn = {2041-1723},
journal = {Nature Communications},
number = {1},
pages = {4620},
title = {Differential lateral and basal tension drive folding of Drosophila wing discs through two distinct mechanisms},
url = {https://doi.org/10.1038/s41467-018-06497-3},
volume = {9},
year = {2018}
}
Weigert, M., Royer, L., Jug, F., & Myers, G. (2017). Isotropic Reconstruction of 3D Fluorescence Microscopy Images Using Convolutional Neural Networks. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).
@inproceedings{weigert2017,
author = {Weigert, Martin and Royer, Loic and Jug, Florian and Myers, Gene},
title = {Isotropic Reconstruction of 3D Fluorescence Microscopy Images Using Convolutional Neural Networks},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
year = {2017},
doi = {10.1007/978-3-319-66185-8_15}
}
Weigert, M., Royer, L., Jug, F., & Myers, G. (2017). Isotropic Reconstruction of 3D Fluorescence Microscopy Images Using CNNs. MICCAI.
@inproceedings{weigert2017_short,
author = {Weigert, Martin and Royer, Loic and Jug, Florian and Myers, Gene},
title = {Isotropic Reconstruction of 3D Fluorescence Microscopy Images Using CNNs},
booktitle = {MICCAI},
year = {2017},
doi = {10.1007/978-3-319-66185-8_15}
}
Patel, A., Lee, H. O., Jawerth, L. M., Maharana, S. K., Jähnel, M., Hein, M. Y., Stoynov, S. S., Mahamid, J., Saha, S., Franzmann, T. M., Pozniakovski, A., Poser, I., Maghelli, N., Royer, L. A., Weigert, M., Myers, E. W., Grill, S. W., Drechsel, D. N., Hyman, A. A., & Alberti, S. (2015). A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation. Cell, 162, 1066–1077.
@article{patel2015,
title = {A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation},
author = {Patel, Avinash and Lee, Hyun Ou and Jawerth, Louise M. and Maharana, S. K. and J{\"a}hnel, Marcus and Hein, Marco Y and Stoynov, Stoyno Stefanov and Mahamid, Julia and Saha, Shambaditya and Franzmann, Titus M and Pozniakovski, Andrej and Poser, Ina and Maghelli, Nicola and Royer, Loic A. and Weigert, Martin and Myers, Eugene W. and Grill, Stephan W and Drechsel, David N. and Hyman, Anthony A. and Alberti, Simon},
journal = {Cell},
year = {2015},
doi = {10.1016/j.cell.2015.07.047},
volume = {162},
pages = {1066-1077}
}
Royer, L. A., Weigert, M., Günther, U., Maghelli, N., Jug, F., Sbalzarini, I. F., & Myers, E. W. (2015). ClearVolume: open-source live 3D visualization for light-sheet microscopy. Nature Methods, 12(6), 480.
@article{royer2015,
title = {ClearVolume: open-source live 3D visualization for light-sheet microscopy},
author = {Royer, Loic A and Weigert, Martin and G{\"u}nther, Ulrik and Maghelli, Nicola and Jug, Florian and Sbalzarini, Ivo F and Myers, Eugene W},
journal = {Nature Methods},
volume = {12},
number = {6},
pages = {480},
doi = {10.1038/nmeth.3372},
year = {2015},
publisher = {Nature Publishing Group}
}