ArticleMedical image analysis2018
A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos.
Article in Medical image analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Intravital mesoscale optical imaging: challenges, techniques, and future perspectives.Biophysics reports · 2026Article
- An automated cell-tracking pipeline for the analysis of neutrophil dynamics.Frontiers in bioinformatics · 2026Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- Pushing the limits of automated cell tracking.Nature methods · 2025Article
- Uncertainty-Gated Min-Cost Flows forbioRxiv : the preprint server for biology · 2025Article
- A review for cell and particle tracking on microscopy images using algorithms and deep learning technologies.Biomedical journal · 2022Article
- ASIST: Annotation-free synthetic instance segmentation and tracking by adversarial simulations.Computers in biology and medicine · 2021Article
- A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.PloS one · 2021Article
- Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap.Biomedical optics express · 2020Article
- Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy.Bioinformatics (Oxford, England) · 2019Article
- Learn to segment single cells with deep distance estimator and deep cell detector.Computers in biology and medicine · 2019Article
- Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes.SLAS discovery : advancing life sciences R & D · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested on eight different time-lapse microscopy data sets, some of which are high-throughput, demonstrating promising results for the detection, segmentation and association of planar cells. Our results surpass the state of the art for the Fluo-C2DL-MSC data set of the Cell Tracking Challenge (Maška et al., 2014).
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Registered trials
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