Cell Tracking
36 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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ALFI
Most implemented papers
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
We demonstrate the efficacy of our method on real-world tracking problems.
Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm.
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation
naivete5656/WSCTBFP
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ECCV 2020
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i. e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
Appearance-free Tripartite Matching for Multiple Object Tracking
We focus on the general MOT problem regardless of the appearance and propose an appearance-free tripartite matching to avoid the irregular velocity problem of the bipartite matching.
Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation
dstallmann/cell_cultivation_analysis
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Motivation: Innovative microfluidic systems carry the promise to greatly facilitate spatio-temporal analysis of single cells under well-defined environmental conditions, allowing novel insights into population heterogeneity and opening new opportunities for fundamental and applied biotechnology.
Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy
We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image.
FastTrack: an open-source software for tracking varying numbers of deformable objects
Analyzing the dynamical properties of mobile objects requires to extract trajectories from recordings, which is often done by tracking movies.
CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images
AnnabelChen51/CellTrack-R-CNN
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Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine.
Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies
Our initial tests using experimental image sequences (i. e., real data) of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.
Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images
To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model.
