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A disector-based framework for the automatic optical fractionator.

Palak Dave1, Dmitry Goldgof1, Lawrence O Hall1

  • 1Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33620, USA.

Journal of Chemical Neuroanatomy
|July 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces AI-driven deep learning methods for automated cell counting in tissue, improving accuracy and efficiency in stereology. The new techniques enable precise quantification of cells, advancing biological research.

Keywords:
Automatic optical fractionatorCell countingDisector stacksMicroscopy image stackOverlapping cell segmentationU-netUnbiased stereology

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Biotechnology

Background:

  • Stereology is the gold standard for quantifying biological objects in tissue sections.
  • Artificial intelligence (AI) and deep learning (DL) offer potential for automating stereology data collection.
  • Previous work demonstrated DL's comparable accuracy, repeatability, and throughput to manual stereology for cell counting in extended depth of field (EDF) images.

Purpose of the Study:

  • To develop novel deep learning (DL) methods for automating cell counting using unbiased stereology.
  • To improve the accuracy and efficiency of automated cell counting without requiring additional expert time.
  • To overcome limitations of existing methods, such as under-counting errors due to overlapping cells.

Main Methods:

  • A semi-automatic approach using a handcrafted Adaptive Segmentation Algorithm (ASA) to generate ground truth data for training DL models.
  • A Multi-channel Input and Multi-channel Output (MIMO) deep learning method utilizing a U-Net architecture for cell counting in z-axis image stacks (disector stacks).
  • Application of these methods for automatic disector-based estimation of NeuN-immunostained neurons in mouse neocortex.

Main Results:

  • The ASA method successfully generated ground truth data, enhancing DL model training and improving accuracy and efficiency.
  • The MIMO U-Net architecture effectively performed automatic cell counting in disector stacks, ensuring accurate counts by spatial separation in the z-plane.
  • The developed methods successfully avoided false negatives from overlapping cells in EDF images and under-counting errors in z-planes.

Conclusions:

  • This work presents the first demonstration of automatic estimation of total cell number in tissue sections using DL combined with the disector-based optical fractionator method.
  • The proposed DL-based automation of the ordinary optical fractionator ensures accurate cell counts, overcoming limitations of previous EDF and 3D DL models.
  • These advances provide practical applications for precise neuronal quantification in neuroscience research.