Author

M. Unser

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 4 Biochemistry· 1 Bioengineering· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 6 Approach:deep learning· 4 Research Focus:benchmarking· 2 Assay:fluorescence / live-cell microscopy· 2 Research Focus:software / tool· 2 Approach:benchmark study· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

D. Sage· 5 shared L. Donati· 2 shared S. Holden· 2 shared A. Agrawal· 1 shared A. Archetti· 1 shared A. Herbert· 1 shared A. Munoz-Barrutia· 1 shared A. Nogueron· 1 shared

Activity by Year

2018-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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5 shared papersBiophysics

D. Sage

Mutual co-authors in cluster

Author profile →

7 papers

Recent·Most cited

Surpassing Light Inhomogeneities in Structured-Illumination Microscopy with FlexSIM

E. Soubies, A. Nogueron, F. Pelletier, T. Mangeat, C. Leterrier, M. Unser, D. Sage

This paper introduces FlexSIM, a SIM reconstruction method that handles imaging challenges and achieves state-of-the-art performance on open datasets.

bioRxiv · Dec 20, 2023Biophysics

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

E. Gomez-de-Mariscal, C. Garcia-Lopez-de-Haro, W. Ouyang, L. Donati, E. Lundberg, M. Unser, A. Munoz-Barrutia, D. Sage

DeepImageJ provides a user-friendly environment for using pre-trained deep learning models in ImageJ for biomedical image analysis tasks.

bioRxiv · May 6, 2021Bioengineering

GRAPHIC: GRAPH-BASED HIERARCHICAL CLUSTERING FOR SINGLE-MOLECULE LOCALIZATION MICROSCOPY

M. Pouria, S. Aziznejad, M. Unser, D. Sage

The paper introduces a graph-based hierarchical clustering method for single-molecule localization microscopy data to analyze biological particles across scales.

bioRxiv · Dec 22, 2020 · PreprintBiophysics

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning

H. Gupta, M. T. McCann, L. Donati, M. Unser

CryoGAN uses deep adversarial learning to reconstruct 3D structures from single-particle cryo-EM data without requiring prior knowledge of particle poses or initial volume estimates.

bioRxiv · Mar 20, 2020Biochemistry

Virtual-SMLM, a virtual environment for real-time interactive SMLM acquisition

J. Griffie, T.-A. M. Pham, C. Sieben, R. Lang, V. Cevher, S. Holden, M. Unser, S. Manley, D. Sage

Virtual-SMLM provides a virtual environment for real-time interactive single molecule localisation microscopy acquisition, enabling training and validation of automated methods with realistic simulated data.

bioRxiv · Mar 5, 2020 · PreprintBiophysics

Super-resolution fight club: A broad assessment of 2D & 3D single-molecule localization microscopy software

D. Sage, T.-A. Pham, H. Babcock, T. Lukes, T. Pengo, J. Chao, R. Velmurugan, A. Herbert, A. Agrawal, S. Colabrese, A. Wheeler, A. Archetti, B. Rieger, R. Ober, G. M. Hagen, , S. Holden

The paper evaluates 36 single-molecule localization microscopy software packages using simulated datasets to assess their performance in 2D and 3D imaging.

bioRxiv · Dec 1, 2018Biophysics

Imaging beyond the super-resolution limits using ultrastructure expansion microscopy (UltraExM)

D. Gambarotto, F. U. Zwettler, M. Cernohorska, D. Fortun, S. Borgers, J. Heine, J. G. Schloetel, M. Reuss, M. Unser, E. S. Boyden, M. Sauer, V. Hamel, P. Guichard

This paper introduces UltraExM, a method that enables subdiffraction-resolution imaging of preserved ultrastructures using standard optical microscopy, revealing new insights into centriolar structures.

bioRxiv · Apr 25, 2018 · PreprintCell Biology