ArticleBMC bioinformatics2013
A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.BMC bioinformatics · 2022Article
- Delineating the mechanisms and design principles ofComputational and structural biotechnology journal · 2022Review
- In vivo large-scale analysis of Drosophila neuronal calcium traces by automated tracking of single somata.Scientific reports · 2020Article
- Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans.Lab animal · 2019Review
- Three-Dimensional Segmentation and Reconstruction of Neuronal Nuclei in Confocal Microscopic Images.Frontiers in neuroanatomy · 2019Article
- Spatial pattern analysis of nuclear migration in remodelled muscles during Drosophila metamorphosis.BMC bioinformatics · 2017Article
- Deciphering tissue morphodynamics using bioimage informatics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.BMC bioinformatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage.
resultsThis paper presents a new type of nuclei segmentation method embracing an bi-directional prediction procedure, which can greatly reduce the number of false negative errors, the most common errors in the previous segmentation. In this method, we first use a 2D region growing technique together with the level-set method to generate accurate 2D slices. Then a modified gradient method instead of the existing 3D local maximum method is adopted to detect all the 2D slices located in the nuclei center, each of which corresponds to one nucleus. Finally, the bi-directional prediction method based on the images before and after the current time point is introduced into the system to predict the nuclei in low quality parts of the images. The result of our method shows a notable improvement in the accuracy rate. For each nucleus, its precise location, volume and gene expression value (gray value) is also obtained, all of which will be useful in further downstream analyses.
conclusionsThe result of this research demonstrates the advantages of the bi-directional prediction method in the nuclei segmentation over that of StarryNite/MatLab StarryNite. Several other modifications adopted in our nuclei segmentation system are also discussed.
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.