Martin Weigert

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Related publications (17)

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

Martin Weigert, Benjamin Tobias Gallusser

Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with autom ...

Rockefeller University Press2022

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

Michaël Unser, Cathrin Brisken, Daniel Sage, Olivier Burri, Quentin Juppet, Fabio De Martino, Martin Weigert

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis gener ...

2021

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

Hui Yang, Martin Weigert

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion Assisted Iterative Fluorescence In Situ H ...

CELL PRESS2021

3D FIB-SEM reconstruction of microtubule-organelle interaction in whole primary mouse beta cells

Martin Weigert

Microtubules play a major role in intracellular trafficking of vesicles in endocrine cells. Detailed knowledge of microtubule organization and their relation to other cell constituents is crucial for understanding cell function. However, their role in insu ...

Rockefeller University Press2021

Deep learning-enhanced light-field imaging with continuous validation

Martin Weigert

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective th ...

2021

A convolutional neural network segments yeast microscopy images with high accuracy

Anne-Florence Raphaëlle Bitbol, Sahand Jamal Rahi, Vojislav Gligorovski, Mateusz Kozinski, Martin Weigert, Nicola Dietler, Matthias Minder, Ahmad Sadeghi

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 i ...

Nature Research2020

Removing Structured Noise With Self-Supervised Blind-Spot Networks

Martin Weigert

Removal of noise from fluorescence microscopy images is an important first step in many biological analysis pipelines. Current state-of-the-art supervised methods employ convolutional neural networks that are trained with clean (ground-truth) images. Recen ...

IEEE2020

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