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20182024
most cited3D fluorescence microscopy data synthesis for segmentation and benchmarking

23 citations · 39 across the 8 of their papers we have counts for

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Showing eess.IVShow all

7 papers · 1 filter

eess.IV2024

Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields

Rüveyda Yilmaz, Dennis Eschweiler, Johannes Stegmaier

The segmentation and tracking of living cells play a vital role within the biomedical domain, particularly in cancer research, drug development, and developmental biology. These ar…

eess.IV2023★ 6 cited

Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation

Yuli Wu, Weidong He, Dennis Eschweiler +5

Modern biomedical image analysis using deep learning often encounters the challenge of limited annotated data. To overcome this issue, deep generative models can be employed to syn…

eess.IV2023★ 4 cited

Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets

Dennis Eschweiler, Rüveyda Yilmaz, Matisse Baumann +5

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particu…

eess.IV2021★ 1 cited

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…

eess.IV2021★ 23 cited

3D fluorescence microscopy data synthesis for segmentation and benchmarking

Dennis Eschweiler, Malte Rethwisch, Mareike Jarchow +2

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproduci…

eess.IV2021★ 2 cited

Robust 3D Cell Segmentation: Extending the View of Cellpose

Dennis Eschweiler, Richard S. Smith, Johannes Stegmaier

Increasing data set sizes of 3D microscopy imaging experiments demand for an automation of segmentation processes to be able to extract meaningful biomedical information. Due to th…

eess.IV2020

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…