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Found 10 papers, 9 papers with code
Trackastra: Transformer-based cell tracking for live-cell microscopy
Importantly, unlike existing transformer-based MOT pipelines, our learning architecture also accounts for dividing objects such as cells and allows for accurate tracking even with simple greedy linking, thus making strides towards removing the requirement for a complex linking step.
Self-supervised dense representation learning for live-cell microscopy with time arrow prediction
Here we present a self-supervised method based on time arrow prediction pre-training that learns dense image representations from raw, unlabeled live-cell microscopy videos.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets
In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation.
Nuclei instance segmentation and classification in histopathology images with StarDist
Instance segmentation and classification of nuclei is an important task in computational pathology.
Practical sensorless aberration estimation for 3D microscopy with deep learning
Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role.
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics
It mimics the pathological assessment and relies on the detection and localization of interphase nuclei based on instance segmentation networks.
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
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.
