Publications de Martin Weigert

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BackgroundHigh-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate detection is key for a ...

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

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 ...

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 ...

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 ...

The concomitant occurrence of tissue growth and organization is a hallmark of organismal development(1-3). This often means that proliferating and differentiating cells are found at the same time in a continuously changing tissue environment. How cells ada ...

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low sig ...

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 ...

Cell tracking is a ubiquitous image analysis task in live-cell microscopy. Unlike multiple object tracking (MOT) for natural images, cell tracking typically involves hundreds of similar-looking objects that can divide in each frame, making it a particularl ...

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 ...

Martin Weigert

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

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

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

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

A convolutional neural network segments yeast microscopy images with high accuracy

Removing Structured Noise With Self-Supervised Blind-Spot Networks

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

Neuronal migration prevents spatial competition in retinal morphogenesis

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

Deep learning-enhanced light-field imaging with continuous validation

TRACKASTRA: Transformer-Based Cell Tracking for Live-Cell Microscopy

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

Event-driven acquisition for content-enriched microscopy