Search Results for author: Florian Jug

Found 24 papers, 14 papers with code

DeepContrast: Deep Tissue Contrast Enhancement using Synthetic Data Degradations and OOD Model Predictions

no code implementations16 Aug 2023 Nuno Pimpão Martins, Yannis Kalaidzidis, Marino Zerial, Florian Jug

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

no code implementations8 Jul 2023 Joran Deschamps, Damian Dalle Nogare, Florian Jug

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

no code implementations29 Mar 2023 Damian Edward Dalle Nogare, Matthew Hartley, Joran Deschamps, Jan Ellenberg, Florian Jug

The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI).

uSplit: Image Decomposition for Fluorescence Microscopy

1 code implementation ICCV 2023 Ashesh Ashesh, Alexander Krull, Moises Di Sante, Francesco Pasqualini, Florian Jug

We present mSplit, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.

μSplit: efficient image decomposition for microscopy data

1 code implementation23 Nov 2022 Ashesh, Alexander Krull, Moises Di Sante, Francesco Silvio Pasqualini, Florian Jug

We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images.

Fourier Image Transformer

1 code implementation6 Apr 2021 Tim-Oliver Buchholz, Florian Jug

Additionally, we show that an encoder-decoder setup can be used to query arbitrary Fourier coefficients given a set of Fourier domain observations.

Computed Tomography (CT) Image Classification +1

Embedding-based Instance Segmentation in Microscopy

1 code implementation25 Jan 2021 Manan Lalit, Pavel Tomancak, Florian Jug

Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications.

Instance Segmentation Segmentation +1

Improving Blind Spot Denoising for Microscopy

2 code implementations19 Aug 2020 Anna S. Goncharova, Alf Honigmann, Florian Jug, Alexander Krull

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.


DenoiSeg: Joint Denoising and Segmentation

1 code implementation6 May 2020 Tim-Oliver Buchholz, Mangal Prakash, Alexander Krull, Florian Jug

Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.

Denoising Few-Shot Learning +1

Leveraging Self-supervised Denoising for Image Segmentation

1 code implementation27 Nov 2019 Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit, Pavel Tomancak, Florian Jug, Alexander Krull

Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images.

Denoising Image Segmentation +2

Fully Unsupervised Probabilistic Noise2Void

1 code implementation27 Nov 2019 Mangal Prakash, Manan Lalit, Pavel Tomancak, Alexander Krull, Florian Jug

Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.

Image Denoising

Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

3 code implementations3 Jun 2019 Alexander Krull, Tomas Vicar, Florian Jug

Self-supervised methods are, unfortunately, not competitive with models trained on image pairs.

Image Denoising

Noise2Void - Learning Denoising from Single Noisy Images

6 code implementations CVPR 2019 Alexander Krull, Tim-Oliver Buchholz, Florian Jug

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.

Image Denoising

Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data

1 code implementation12 Oct 2018 Tim-Oliver Buchholz, Mareike Jordan, Gaia Pigino, Florian Jug

Cryo-transmission electron microscopy (cryo-TEM) could profoundly benefit from improved denoising methods, unfortunately it is one of the latter.

Cryogenic Electron Microscopy (cryo-EM) Denoising +1

Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

no code implementations5 Apr 2017 Martin Weigert, Loic Royer, Florian Jug, Gene Myers

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

no code implementations21 Feb 2017 Dmitrij Schlesinger, Florian Jug, Gene Myers, Carsten Rother, Dagmar Kainmüller

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.

Image Segmentation Segmentation +1

Efficient Algorithms for Moral Lineage Tracing

no code implementations ICCV 2017 Markus Rempfler, Jan-Hendrik Lange, Florian Jug, Corinna Blasse, Eugene W. Myers, Bjoern H. Menze, Bjoern Andres

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

no code implementations CVPR 2016 Florian Jug, Evgeny Levinkov, Corinna Blasse, Eugene W. Myers, Bjoern Andres

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).


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