23 citations · 110 across the 24 of their papers we have counts for
27 papers
SortedAP: Rethinking evaluation metrics for instance segmentation
Long Chen, Yuli Wu, Johannes Stegmaier +1
Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties,…
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs
Firas Khader, Gustav Müller-Franzes, Tianyu Han +4
Computed Tomography (CT) scans provide detailed and accurate information of internal structures in the body. They are constructed by sending x-rays through the body from different…
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
Firas Khader, Jakob Nikolas Kather, Tianyu Han +4
Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. An automated analysis of such images using deep learning models is…
A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
Yuli Wu, Ivan Karetic, Johannes Stegmaier +2
We propose a neural network-based framework to optimize the perceptions simulated by the in silico retinal implant model pulse2percept. The overall pipeline consists of a trainable…
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…
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis
Firas Khader, Gustav Mueller-Franzes, Tianci Wang +9
Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn…
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh +12
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input…
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…
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…
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…
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…
Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE
Dennis Eschweiler, Johannes Stegmaier
The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei. In the preprocessing phase, we detect centroids o…
CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images
Dennis Eschweiler, Thiago V. Spina, Rohan C. Choudhury +3
The quantitative analysis of cellular membranes helps understanding developmental processes at the cellular level. Particularly 3D microscopic image data offers valuable insights i…
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer
Nadezhda Prodanova, Johannes Stegmaier, Stephan Allgeier +5
Transfer learning is a powerful tool to adapt trained neural networks to new tasks. Depending on the similarity of the original task to the new task, the selection of the cut-off l…
SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
Thiago V. Spina, Johannes Stegmaier, Alexandre X. Falcão +2
The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is…
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Johannes Stegmaier, Thiago V. Spina, Alexandre X. Falcão +4
Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provi…
The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide
Ralf Mikut, Andreas Bartschat, Wolfgang Doneit +5
The Matlab toolbox SciXMiner is designed for the visualization and analysis of time series and features with a special focus to classification problems. It was developed at the Ins…
3D Cell Nuclei Segmentation with Balanced Graph Partitioning
Julian Arz, Peter Sanders, Johannes Stegmaier +1
Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images. With ever-growing sizes and amounts of three-dimensional images to be processed, t…
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Johannes Stegmaier
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hard…
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Johannes Stegmaier, Ralf Mikut
Many automatically analyzable scientific questions are well-posed and offer a variety of information about the expected outcome a priori. Although often being neglected, this prior…