1.7k citations · 2.5k across the 12 of their papers we have counts for
12 papers
Unsupervised Learning of Cell Instances with Generative Routing Pyramids
Ziwen Liu, Martin Weigert
Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised…
MuViT: Multi-Resolution Vision Transformers for Learning Across Scales in Microscopy
Albert Dominguez Mantes, Gioele La Manno, Martin Weigert
Modern microscopy routinely produces gigapixel images that contain structures across multiple spatial scales, from fine cellular morphology to broader tissue organization. Many ana…
Self-supervised dense representation learning for live-cell microscopy with time arrow prediction
Benjamin Gallusser, Max Stieber, Martin Weigert
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. H…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets
Andreas Müller, Deborah Schmidt, Lucas Rieckert +2
Volume electron microscopy is the method of choice for the in-situ interrogation of cellular ultrastructure at the nanometer scale. Recent technical advances have led to a rapid in…
Nuclei instance segmentation and classification in histopathology images with StarDist
Martin Weigert, Uwe Schmidt
Instance segmentation and classification of nuclei is an important task in computational pathology. We show that StarDist, a deep learning nuclei segmentation method originally dev…
Practical sensorless aberration estimation for 3D microscopy with deep learning
Debayan Saha, Uwe Schmidt, Qinrong Zhang +6
Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise a…
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics
Sarah Schmell, Falk Zakrzewski, Walter de Back +10
Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order t…
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Martin Weigert, Uwe Schmidt, Robert Haase +2
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects. Although many auto…
Cell Detection with Star-convex Polygons
Uwe Schmidt, Martin Weigert, Coleman Broaddus +1
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per…
Biobeam - Rigorous wave-optical simulations of light-sheet microscopy
Martin Weigert, Eugene W. Myers, Moritz Kreysing
Sample-induced image-degradation remains an intricate wave-optical problem in light-sheet microscopy. Here we present biobeam, an open-source software package that enables to simul…
Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks
Martin Weigert, Loic Royer, Florian Jug +1
Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative…