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Found 30 papers, 8 papers with code
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields
It is composed of a denoising diffusion probabilistic model (DDPM) generating high-fidelity synthetic cell microscopy images and a flow prediction model (FPM) predicting the non-rigid transformation between consecutive video frames.
Optimizing Retinal Prosthetic Stimuli with Conditional Invertible Neural Networks
However, the information transmission between the camera and retinal cells is often limited by the low resolution of the electrode array and the lack of specificity for different ganglion cell types, resulting in suboptimal stimulations.
Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time
Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation.
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation
Modern biomedical image analysis using deep learning often encounters the challenge of limited annotated data.
SortedAP: Rethinking evaluation metrics for instance segmentation
Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy.
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs
X-rays are widely available and even if the CT reconstructed from these radiographs is not a replacement of a complete CT in the diagnostic setting, it might serve to spare the patients from radiation where a CT is only acquired for rough measurements such as determining organ size.
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
However, while the conventional transformer allows for a simultaneous processing of a large set of input tokens, the computational demand scales quadratically with the number of input tokens and thus quadratically with the number of image patches.
A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
The pre-trained retinal implant model is also a U-Net, which is trained to mimic the biomimetic perceptual model implemented in pulse2percept.
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
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 particularly effective method.
Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis
Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data.
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0. 91 vs. 0. 95 without vs. with synthetic data).
Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks
Recent developments in fluorescence microscopy allow capturing high-resolution 3D images over time for living model organisms.
3D fluorescence microscopy data synthesis for segmentation and benchmarking
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 reproducible way.
Robust 3D Cell Segmentation: Extending the View of Cellpose
Increasing data set sizes of 3D microscopy imaging experiments demand for an automation of segmentation processes to be able to extract meaningful biomedical information.
Spherical Harmonics for Shape-Constrained 3D Cell Segmentation
Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data.
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs
Automatic analysis of spatio-temporal microscopy images is inevitable for state-of-the-art research in the life sciences.
Making Logic Learnable With Neural Networks
We show the utility of our pipeline on a network that is trained on biomedical data.
Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
Current in vivo microscopy allows us detailed spatiotemporal imaging (3D+t) of complete organisms and offers insights into their development on the cellular level.
Towards Automatic Embryo Staging in 3D+T Microscopy Images using Convolutional Neural Networks and PointNets
Automatic analyses and comparisons of different stages of embryonic development largely depend on a highly accurate spatiotemporal alignment of the investigated data sets.
Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE
The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei.
CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images
The quantitative analysis of cellular membranes helps understanding developmental processes at the cellular level.
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer
Transfer learning is a powerful tool to adapt trained neural networks to new tasks.
SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects.
Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection
Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images.
The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide
The decision to a Matlab-based solution was made to use the wide mathematical functionality of this package provided by The Mathworks Inc. SciXMiner is controlled by a graphical user interface (GUI) with menu items and control elements like popup lists, checkboxes and edit elements.
3D Cell Nuclei Segmentation with Balanced Graph Partitioning
Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images.
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions.
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines.
Generating Semi-Synthetic Validation Benchmarks for Embryomics
Systematic validation is an essential part of algorithm development.
Challenges of Integrating A Priori Information Efficiently in the Discovery of Spatio-Temporal Objects in Large Databases
The described framework can be used to gain knowledge out of object databases in many different fields.
