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A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.

Athul Vijayan1, Tejasvinee Atul Mody1, Qin Yu2,3

  • 1Plant Developmental Biology, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany.

Development (Cambridge, England)
|July 22, 2024
PubMed
Summary

We developed computational tools for accurate 3D nuclear segmentation in digital organs. Our models and dataset aid researchers in analyzing plant and animal tissues, improving cell segmentation accuracy.

Keywords:
Arabidopsis3D digital organ3D nuclear segmentationOvulePlant cellsPlantSegStarDist

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Area of Science:

  • * Biology
  • * Computational Biology
  • * Microscopy

Background:

  • * Accurate 3D segmentation of nuclei is crucial for understanding cellular structures and functions in complex biological tissues.
  • * Existing computational tools often require extensive customization or lack applicability across diverse sample types and imaging conditions.
  • * Advanced analysis of 3D digital organs necessitates robust methods for nuclear segmentation and integration with cellular context.

Purpose of the Study:

  • * To develop and validate a versatile computational framework for accurate 3D nuclear segmentation in various biological samples.
  • * To create high-quality, reusable models and datasets for training and applying 3D nuclear segmentation algorithms.
  • * To enhance 3D image analysis software for improved cellular and nuclear segmentation and analysis in digital organs.

Main Methods:

  • * Developed a ground truth generation approach and iterative training strategy for 3D nuclear segmentation models.
  • * Applied and fine-tuned popular algorithms (CellPose, PlantSeg, StarDist) using custom-trained models.
  • * Integrated 3D segmented nuclei with surrounding cells in MorphoGraphX and extended PlantSeg with a proofreading tool.

Main Results:

  • * Provided two high-quality models for 3D nuclear segmentation applicable to fixed/live plant and animal tissues with diverse staining.
  • * Shared a comprehensive training dataset of approximately 10,000 nuclei.
  • * Demonstrated that the nuclear-to-cell volume ratio varies across ovule tissues and developmental stages.
  • * Enhanced MorphoGraphX for linking nuclei to cells and improved PlantSeg with a nucleus-seeded cell segmentation correction tool.

Conclusions:

  • * The developed computational tools and models significantly improve the accuracy and applicability of 3D nuclear segmentation across diverse biological contexts.
  • * The shared dataset and enhanced software facilitate advanced quantitative analysis of cellular and nuclear morphology in 3D digital organs.
  • * This work provides a valuable resource for researchers studying tissue development, cellular dynamics, and nuclear organization in plants and animals.