2 citations · 5 across the 6 of their papers we have counts for
14 papers
Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks
Canyu Yang, Dennis Eschweiler, Johannes Stegmaier
Recent developments in fluorescence microscopy allow capturing high-resolution 3D images over time for living model organisms. To be able to image even large specimens, techniques…
Spherical Harmonics for Shape-Constrained 3D Cell Segmentation
Dennis Eschweiler, Malte Rethwisch, Simon Koppers +1
Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. To process the vast amount of image data generated by current digitized imaging te…
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs
Dennis Bähr, Dennis Eschweiler, Anuk Bhattacharyya +3
Automatic analysis of spatio-temporal microscopy images is inevitable for state-of-the-art research in the life sciences. Recent developments in deep learning provide powerful tool…
Making Logic Learnable With Neural Networks
Tobias Brudermueller, Dennis L. Shung, Adrian J. Stanley +2
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally…
Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
Sourabh Bhide, Ralf Mikut, Maria Leptin +1
Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level. Even though the…
Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets
Manuel Traub, Johannes Stegmaier
Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatiotemporal alignment of the investigated data sets. In this…