Evidence map›Paper›PMID 24252066›Full record

ArticleBMC bioinformatics2013

A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images.

Long Chen, Leanne Lai Hang Chan, Zhongying Zhao, Hong Yan

Abstract read
In one paragraph

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.

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0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Delineating the mechanisms and design principles ofComputational and structural biotechnology journal · 2022
    Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Deciphering tissue morphodynamics using bioimage informatics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017
    Review
  8. Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Long ChenDepartment of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. longchen5@alumni.cityu.edu.hk.
Leanne Lai Hang Chan
Zhongying Zhao
Hong Yan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

AlgorithmsAnimalsCaenorhabditis elegansCell NucleusImaging, Three-DimensionalMicroscopy, ConfocalTime-Lapse Imaging

Identifiers

PMID24252066
PMCPMC3903074

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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.