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Related Concept Videos

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...

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Related Experiment Video

Updated: May 15, 2026

A High-Throughput In Situ Method for Estimation of Hepatocyte Nuclear Ploidy in Mice
08:44

A High-Throughput In Situ Method for Estimation of Hepatocyte Nuclear Ploidy in Mice

Published on: April 19, 2020

Efficient nucleus detector in histopathology images.

J P Vink1, M B Van Leeuwen, C H M Van Deurzen

  • 1Video and Image Processing Group, Philips Research, Eindhoven, The Netherlands. jelte.peter.vink@philips.com

Journal of Microscopy
|December 21, 2012
PubMed
Summary

This study introduces an efficient machine learning nucleus detector for digital pathology. It achieves high accuracy in detecting nuclei in breast cancer images, enabling faster, more objective cancer diagnosis.

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Using Computer Vision Libraries to Streamline Nuclei Quantification
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Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

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Last Updated: May 15, 2026

A High-Throughput In Situ Method for Estimation of Hepatocyte Nuclear Ploidy in Mice
08:44

A High-Throughput In Situ Method for Estimation of Hepatocyte Nuclear Ploidy in Mice

Published on: April 19, 2020

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Area of Science:

  • Digital pathology
  • Computational pathology
  • Machine learning in healthcare

Background:

  • Traditional cancer diagnosis relies on subjective pathologist analysis of biopsy samples, leading to variability.
  • Digital pathology offers potential for automated, objective assessment, improving quality and reducing analysis time.
  • Accurate nucleus detection is fundamental for automated analysis of histopathological images.

Purpose of the Study:

  • To develop an efficient nucleus detector for automated assessment of histopathological images using machine learning.
  • To improve the computational efficiency and accuracy of nucleus detection algorithms.
  • To enable objective and rapid analysis of digital pathology slides.

Main Methods:

  • Applied color deconvolution to reconstruct stains from histopathological images.
  • Developed two nucleus detectors using a modified AdaBoost algorithm, incorporating feature computational cost for efficiency.
  • Merged detector outputs using a globally optimal active contour algorithm for precise nucleus border delineation.

Main Results:

  • Achieved a 95% nucleus detection rate on Her2 immunohistochemistry stained breast tissue images.
  • Demonstrated an average of 58 false positives per field-of-view across 51 fields-of-view.
  • Completed analysis in 1 second per field-of-view, showcasing high computational efficiency.

Conclusions:

  • The proposed nucleus detector demonstrates strong performance in accuracy and speed for digital pathology.
  • The machine learning approach enhances objectivity and reduces variability in cancer diagnosis.
  • This technology has the potential to significantly advance automated assessment in histopathology.