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Related Experiment Video

Updated: Oct 25, 2025

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
09:33

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

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User friendly, cloud based, whole slide image segmentation.

Brendon Lutnick1, Avinash Kammardi Shashiprakash1, David Manthey2

  • 1Department of Pathology and Anatomical Sciences, SUNY Buffalo.

Proceedings of Spie--The International Society for Optical Engineering
|August 9, 2021
PubMed
Summary
This summary is machine-generated.

We developed an easy-to-use plugin for segmenting whole slide images (WSIs) using cloud-based convolutional neural networks. This tool enhances accessibility for clinicians and researchers in digital pathology.

Keywords:
WSI segmentationcloud based analysisglomeruliplugin

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Area of Science:

  • Digital Pathology
  • Computational Biology
  • Medical Image Analysis

Background:

  • Convolutional neural networks (CNNs) excel at image segmentation but are difficult for non-experts to use in histology.
  • Current tools require complex setups and have underdeveloped interfaces, limiting clinical and biological researcher adoption.
  • Whole slide images (WSIs) present unique challenges for automated analysis.

Purpose of the Study:

  • To develop a user-friendly plugin for WSI segmentation using CNNs.
  • To improve the accessibility and translatability of advanced image analysis techniques for researchers and clinicians.
  • To enable remote, cloud-based segmentation analysis of pathological images.

Main Methods:

  • Developed an open-source plugin integrating a state-of-the-art CNN for WSI segmentation.
  • Built upon the HistomicsTK platform for remote data management and viewing of WSI datasets.
  • Implemented a cloud-based architecture allowing remote analysis via a graphical user interface (GUI).

Main Results:

  • The plugin provides an intuitive GUI for WSI segmentation, accessible over the internet.
  • Demonstrated proof of concept by successfully training the model to segment glomeruli in renal tissue images.
  • The system facilitates remote analysis, reducing the need for extensive local computational resources.

Conclusions:

  • The developed plugin significantly lowers the barrier to entry for using advanced CNN-based segmentation in pathology.
  • This tool democratizes access to sophisticated image analysis for a broader range of biomedical researchers.
  • The open-source, cloud-based approach promotes wider adoption and application in pathological research.