ArticleProceedings. IEEE International Symposium on Biomedical Imaging2020
Short Trajectory Segmentation With 1D Unet Framework: Application To Secretory Vesicle Dynamics.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- MSP-tracker: A versatile vesicle tracking software tool used to reveal the spatial control of polarized secretion in Drosophila epithelial cells.PLoS biology · 2025Article
- Neural network informed photon filtering reduces fluorescence correlation spectroscopy artifacts.Biophysical journal · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of protein transport in living cell requires automated techniques to capture and quantify dynamics of the protein packaged into secretory vesicles. The movement of the vesicles is not consistent along the trajectory, therefore the quantitative study of their dynamics requires trajectories segmentation. This paper explores quantification of such vesicle dynamics and introduces a novel 1D U-Net based trajectory segmentation. Unlike existing mean squared displacement based methods, our proposed framework is not restricted under the requirement of long trajectories for effective segmentation. Moreover, as our approach provides segmentation within each sliding window, it enables effectively capture even short segments. The approach is quantified by the data acquired from spinning disk microscopy imaging of protein trafficking in
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.