Additionally, we show that an encoder-decoder setup can be used to query arbitrary Fourier coefficients given a set of Fourier domain observations.
In this work we show, for the first time, that generative diversity denoising (GDD) approaches can learn to remove structured noises without supervision.
Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications.
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.
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks.
Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.
We demonstrate the efficacy of our method on real-world tracking problems.
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy 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-transmission electron microscopy (cryo-TEM) could profoundly benefit from improved denoising methods, unfortunately it is one of the latter.
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.
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.
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.
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).