Weigert group – Bioimage Analysis and Computational Microscopy

My group focuses on image-based machine-learning (AI) approaches to extract quantitative biological information from microscopy images. For this we develop new computer vision methods of that are robust and problem-adapted to the biological questions at hand.

Concretely we are interested in

  • Object detection & segmentation: How to delineate biological objects (cell, nuclei) in large 2D and 3D images?
  • Computational Microscopy: How to augment microscopes with problem adapted computer vision methods?
  • Self–supervised learning: How to extract knowledge from microscopy images and time series without (or with only few) manual annotations


[email protected]

SV IBI – AAB 139

Access map

Neuronal migration prevents spatial competition in retinal morphogenesis

M. Rocha-Martins; E. Nerli; J. Kretzschmar; M. Weigert; J. Icha et al. 

Nature. 2023-08-09. Vol. 620, p. 615–624. DOI : 10.1038/s41586-023-06392-y.

CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets

L. Burgy; M. Weigert; G. Hatzopoulos; M. Minder; A. Journe et al. 

Bmc Bioinformatics. 2023-03-28. Vol. 24, num. 1, p. 120. DOI : 10.1186/s12859-023-05214-2.

Event-driven acquisition for content-enriched microscopy

D. Mahecic; W. L. Stepp; C. Zhang; J. Griffie; M. Weigert et al. 

2023-02-10.  p. 16A-16A.

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

B. Gallusser; M. Stieber; M. Weigert 

2023-01-01. 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Vancouver, CANADA, OCT 08-12, 2023. p. 537-547. DOI : 10.1007/978-3-031-43993-3_52.

MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology

R. Sarkis; O. Burri; C. Royer-Chardon; F. Schyrr; S. Blum et al. 

Modern Pathology. 2023. Vol. 36, num. 4, p. 100088. DOI : 10.1016/j.modpat.2022.100088.

Deep neural network automated segmentation of cellular structures in volume electron microscopy

B. Gallusser; G. Maltese; G. Di Caprio; T. J. Vadakkan; A. Sanyal et al. 

Journal Of Cell Biology. 2022-12-05. Vol. 222, num. 2, p. e202208005. DOI : 10.1083/jcb.202208005.

Event-driven acquisition for content-enriched microscopy

D. Mahecic; W. L. Stepp; C. Zhang; J. Griffie; M. Weigert et al. 

Nature Methods. 2022-09-08. Vol. 19, p. 1262–1267. DOI : 10.1038/s41592-022-01589-x.

EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization

Y. Wang; M. Eddison; G. Fleishman; M. Weigert; S. Xu et al. 

Cell. 2021-12-22. Vol. 184, num. 26, p. 6361-+. DOI : 10.1016/j.cell.2021.11.024.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Q. Juppet; F. De Martino; E. Marcandalli; M. Weigert; O. Burri et al. 

Journal of Mammary Gland Biology and Neoplasia. 2021-05-17. Vol. 26, p. 101–112. DOI : 10.1007/s10911-021-09485-4.

Deep learning-enhanced light-field imaging with continuous validation

N. Wagner; F. Beuttenmueller; N. Norlin; J. Gierten; J. C. Boffi et al. 

Nature Methods. 2021-05-01. Vol. 18, num. 5, p. 557-563. DOI : 10.1038/s41592-021-01136-0.