30 citations · 90 across the 21 of their papers we have counts for
21 papers
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…
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…
On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation
Mareike Thies, Fabian Wagner, Noah Maul +6
Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensat…
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…
Segmentation-Guided Knee Radiograph Generation using Conditional Diffusion Models
Siyuan Mei, Fuxin Fan, Fabian Wagner +4
Deep learning-based medical image processing algorithms require representative data during development. In particular, surgical data might be difficult to obtain, and high-quality…
BigReg: An Efficient Registration Pipeline for High-Resolution X-Ray and Light-Sheet Fluorescence Microscopy
Siyuan Mei, Fuxin Fan, Mareike Thies +9
Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as pivotal tools in preclinical research, particularly for studying bone remodeling dis…
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
Noah Maul, Annette Birkhold, Fabian Wagner +5
Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) rec…
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
Mareike Thies, Fabian Wagner, Noah Maul +9
Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such a…
Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN
Jonas Utz, Tobias Weise, Maja Schlereth +5
Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, e…
Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography
Felix Denzinger, Michael Wels, Oliver Taubmann +10
Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd's Crook (SC)…
Exploring Epipolar Consistency Conditions for Rigid Motion Compensation in In-vivo X-ray Microscopy
Mareike Thies, Fabian Wagner, Mingxuan Gu +10
Intravital X-ray microscopy (XRM) in preclinical mouse models is of vital importance for the identification of microscopic structural pathological changes in the bone which are cha…
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Noah Maul, Katharina Zinn, Fabian Wagner +6
Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurat…
Optimizing CT Scan Geometries With and Without Gradients
Mareike Thies, Fabian Wagner, Noah Maul +4
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image. Rigid patient motion is a cause fo…
Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography
Fabian Wagner, Mareike Thies, Noah Maul +5
The diagnostic quality of computed tomography (CT) scans is usually restricted by the induced patient dose, scan speed, and image quality. Sparse-angle tomographic scans reduce rad…
Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
Fabian Wagner, Mareike Thies, Laura Pfaff +10
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usual…
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
Mareike Thies, Fabian Wagner, Noah Maul +10
Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the proje…
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
Fabian Wagner, Mareike Thies, Laura Pfaff +9
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low…
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Fabian Wagner, Mareike Thies, Felix Denzinger +7
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(…
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
Mingxuan Gu, Sulaiman Vesal, Mareike Thies +5
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Contrastive learning (CL) in the context of UDA…
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Fabian Wagner, Mareike Thies, Mingxuan Gu +9
Computed tomography is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution and radiation dose…
Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations
Mareike Thies, Fabian Wagner, Mingxuan Gu +3
Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data. This way, t…