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Using Computer Vision Libraries to Streamline Nuclei Quantification
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Efficient automatic 3D segmentation of cell nuclei for high-content screening.

Mariusz Marzec1, Adam Piórkowski2, Arkadiusz Gertych3,4

  • 1Faculty of Science and Technology, Institute of Biomedical Engineering, University of Silesia, Bedzinska St. 39, 41-200, Sosnowiec, Poland. mariusz.marzec@us.edu.pl.

BMC Bioinformatics
|June 1, 2022
PubMed
Summary

We developed a fast, accurate 3D nuclei segmentation algorithm for high-content screening (HCS). This method reliably delineates cell nuclei in complex 3D images, improving drug efficacy assessment.

Keywords:
3D nuclei segmentationAutomated analysisBio-image informaticsHigh-content screeningImage analysisImage processing

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Area of Science:

  • * Biomedical imaging
  • * Computational biology

Background:

  • * High-content screening (HCS) uses 3D microscopy for drug efficacy assessment.
  • * Accurate cell nuclei segmentation in 3D images is crucial but challenging due to image artifacts and cell variations.
  • * Existing 3D segmentation algorithms lack robustness for HCS applications.

Purpose of the Study:

  • * To develop a robust and efficient 3D nuclei segmentation algorithm for HCS.
  • * To overcome challenges in segmenting nuclei in 3D images with varying cell confluency and morphology.
  • * To improve the accuracy and speed of nuclei detection and delineation in pre-clinical drug screening.

Main Methods:

  • * A novel algorithm combining 3D nuclear mask generation with iterative marker-controlled watershed segmentation.
  • * Segmentation refinement using local nucleus and background intensities in original 3D images.
  • * Developed and tested on 3D images from HCS experiments.

Main Results:

  • * Achieved superior nuclei detection performance: precision (0.97), recall (0.88), and F1-score (0.93) on a large dataset (2,367 nuclei).
  • * Demonstrated comparable nuclei delineation accuracy (Jaccard index: 0.83) to reference methods.
  • * Outperformed reference methods in speed, being over twice as fast as the best-performing alternative.
  • * Maintained high performance on heterogeneous, stacked 3D cell images.

Conclusions:

  • * The proposed algorithm reliably delineates cell nuclei in 3D images.
  • * Suitable for analyzing both monolayered and stacked cells exposed to cytotoxic drugs in HCS.
  • * Offers a significant advancement in automated image analysis for pre-clinical drug discovery.