Publications de 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, Can Aztekin, Irina Khven, Eftychia Kyriacou, Albert Dominguez Mantes, Evangelia Skoufa, Martin Weigert, Georgios Tsissios

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

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

2023

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

Martin Weigert, Benjamin Tobias Gallusser, Max Stieber

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

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, Willi Leopold Stepp, Chen Zhang, Martin Weigert, Juliette Griffie

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