7 papers
Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising
Yipeng Sun, Linda-Sophie Schneider, Siyuan Mei +8
Noise in low-dose computed tomography (LDCT) can obscure important diagnostic details. While deep learning offers powerful denoising, supervised methods require impractical paired…
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…
Learning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction
Yipeng Sun, Linda-Sophie Schneider, Chengze Ye +4
Cone-Beam Computed Tomography (CBCT) is essential in medical imaging, and the Feldkamp-Davis-Kress (FDK) algorithm is a popular choice for reconstruction due to its efficiency. How…
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…
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
Yipeng Sun, Linda-Sophie Schneider, Fuxin Fan +6
In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework. This method over…