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Related Experiment Video

Updated: Aug 13, 2025

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Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on

Luca Cerny Oliveira1, Zhengfeng Lai1, Danielle Harvey2

  • 1Department of Electrical and Computer Engineering, University of California Davis, Davis, California, USA.

Journal of Neuropathology and Experimental Neurology
|January 24, 2023
PubMed
Summary
This summary is machine-generated.

Preanalytical variables like scanner type and magnification significantly impact machine learning (ML) performance in neuropathology. Optimizing these factors is crucial for reliable automated analysis of brain tissue slides.

Keywords:
Alzheimer diseaseAmyloid-βDeep learningDigital pathologyMachine learningSlide scannerWhole slide imaging

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Area of Science:

  • Neuropathology
  • Computational Pathology
  • Digital Pathology

Background:

  • Machine learning (ML) and deep learning (DL) offer scalable analysis for neuropathology.
  • Existing DL frameworks show potential for quantifying amyloid-β deposits and segmenting white matter (WM) from gray matter (GM).
  • These frameworks are often trained and validated on data with limited preanalytical variability.

Purpose of the Study:

  • To evaluate the impact of preanalytical variables on DL framework performance.
  • To assess scanner type, magnification, compression rate, and storage format effects.
  • To analyze performance in WM/GM segmentation and amyloid-β plaque classification tasks.

Main Methods:

  • Utilized three digital slide scanners (Zeiss Axioscan Z1, Leica Aperio AT2, Leica Aperio GT 450).
  • Analyzed over 60 whole slide images from 14 cases with varying amyloid-β deposits.
  • Employed statistical comparisons, including repeated measures analysis of variance, on outputs from two DL frameworks.

Main Results:

  • Significant differences in WM/GM segmentation and amyloid-β plaque classification were observed based on scanner type (p < 0.05).
  • Magnification also showed a statistically significant impact on both tasks (p < 0.05).
  • Compression rate and storage format effects were also evaluated.

Conclusions:

  • Preanalytical variables, specifically scanner type and magnification, demonstrably affect ML algorithm performance in neuropathology.
  • This pilot study underscores the importance of standardizing preanalytical conditions for reliable DL applications.
  • Further research is needed to optimize these variables for robust computational pathology workflows.