most citedDiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling

4 citations · 4 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2025

CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence Microscopy

Jonas Utz, Stefan Vocht, Anne Tjorven Buessen +6

In recent years, numerous neural network architectures specifically designed for the instance segmentation of nuclei in microscopic images have been released. These models embed nu…

cond-mat.mtrl-sci2025

Correlative X-ray and electron tomography for scale-bridging, quantitative analysis of complex, hierarchical particle systems

Alexander Götz, Fabian Lutter, Dennis Simon Possart +18

This study presents a comprehensive workflow for investigating particulate materials through combined 360° electron tomography (ET), nano-computed X-ray tomography (nanoCT), and mi…

cond-mat.mtrl-sci2025★ 4 cited

DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling

Dennis Possart, Leonid Mill, Florian Vollnhals +11

Nanomaterials exhibit distinctive properties governed by parameters such as size, shape, and surface characteristics, which critically influence their applications and interactions…

cs.CV2024

Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images

Mareike Thies, Noah Maul, Siyuan Mei +8

Motion artifacts can compromise the diagnostic value of computed tomography (CT) images. Motion correction approaches require a per-scan estimation of patient-specific motion patte…