Publications by Martin Weigert

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

Martin Weigert, Benjamin Tobias Gallusser

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 ...

Springer Science and Business Media Deutschland GmbH2025

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

Joachim Lingner, Suliana Manley, Gioele La Manno, Irina Khven, Martin Weigert, Eftychia Kyriacou, Can Aztekin, Albert Dominguez Mantes, Georgios Tsissios, Evangelia Skoufa

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 ...

2025

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

Martin Weigert

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 ...

Nature Portfolio2024

Neuronal migration prevents spatial competition in retinal morphogenesis

Martin Weigert

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 ...

NATURE PORTFOLIO2023

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

Olivier Burri, Giovanna Ambrosini, Bart Deplancke, Arne Seitz, Olaia Maria Naveiras Torres-Quiroga, Frédérica Schyrr, Rita Sarkis, Mariangela Costanza, Martin Weigert

2023

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

Max Stieber, Martin Weigert, Benjamin Tobias Gallusser

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 ...

Springer International Publishing Ag2023

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

Pierre Gönczy, Georgios Hatzopoulos, Sahand Jamal Rahi, Martin Weigert

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 ...

BMC2023

Event-driven acquisition for content-enriched microscopy

Suliana Manley, Dora Mahecic, Juliette Griffie, Chen Zhang, Martin Weigert, Willi Leopold Stepp

CELL PRESS2023

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

Martin Weigert

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 ...

Elsevier2023

Event-driven acquisition for content-enriched microscopy

Suliana Manley, Dora Mahecic, Juliette Griffie, Chen Zhang, Martin Weigert, Willi Leopold Stepp

Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution. A common goal ...

2022

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, Daniel Sage, Olivier Burri, Cathrin Brisken, 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

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

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