Search Results for author:
Found 27 papers, 14 papers with code
denoiSplit: a method for joint image splitting and unsupervised denoising
In this work we present denoiSplit, a method to tackle a new analysis task, i. e. the challenge of joint semantic image splitting and unsupervised denoising.
Enabling Global Image Data Sharing in the Life Sciences
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences.
Live-cell imaging powered by computation
The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate.
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets.
DeepContrast: Deep Tissue Contrast Enhancement using Synthetic Data Degradations and OOD Model Predictions
We demonstrated that networks trained in this way can be used out-of-distribution (OOD) to improve the quality of less severely degraded images, e. g. the raw data imaged in a microscope.
Better Research Software Tools to Elevate the Rate of Scientific Discovery -- or why we need to invest in research software engineering
In the past decade, enormous progress has been made in advancing the state-of-the-art in bioimage analysis - a young computational field that works in close collaboration with the life sciences on the quantitative analysis of scientific image data.
bAIoimage analysis: elevating the rate of scientific discovery -- as a community
The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI).
Community-developed checklists for publishing images and image analysis
Images document scientific discoveries and are prevalent in modern biomedical research.
uSplit: Image Decomposition for Fluorescence Microscopy
We present mSplit, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.
μSplit: efficient image decomposition for microscopy data
We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture
We validate our modifications on a range of microscopy and natural image data.
Fourier Image Transformer
Additionally, we show that an encoder-decoder setup can be used to query arbitrary Fourier coefficients given a set of Fourier domain observations.
Interpretable Unsupervised Diversity Denoising and Artefact Removal
This work presents an interpretable approach for unsupervised and diverse image restoration.
Embedding-based Instance Segmentation in Microscopy
Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications.
Improving Blind Spot Denoising for Microscopy
Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images.
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
DenoiSeg: Joint Denoising and Segmentation
Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
We demonstrate the efficacy of our method on real-world tracking problems.
Fully Unsupervised Probabilistic Noise2Void
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.
Leveraging Self-supervised Denoising for Image Segmentation
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images.
Probabilistic Noise2Void: Unsupervised Content-Aware Denoising
Self-supervised methods are, unfortunately, not competitive with models trained on image pairs.
Noise2Void - Learning Denoising from Single Noisy Images
The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images.
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data
Cryo-transmission electron microscopy (cryo-TEM) could profoundly benefit from improved denoising methods, unfortunately it is one of the latter.
Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks
We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to.
Crowd Sourcing Image Segmentation with iaSTAPLE
In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations.
Efficient Algorithms for Moral Lineage Tracing
Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task.
Moral Lineage Tracing
We propose an integer linear program (ILP) whose feasible solutions define a decomposition of each image in a sequence into cells (segmentation), and a lineage forest of cells across images (tracing).
