What is it about?

To understand neuronal survival after a hypoxic-ischemic (HI) attack on the neonatal brain, it is imperative to distinguish between the healthy and dying neurons/cells in histological brain images of HI sheep models. This paper took the first step towards the automated classification of healthy and dying cells in HI images. The paper proposes a novel machine-learning method for that purpose.

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Why is it important?

The histological analysis of neuronal survival after imposing a HI attack on animal models is a manual assessment process. It is a time-consuming task that is very much subjective to human perspectives, introducing inter-rater variability in the assessment. Thus, the need to automate the process that will bring much-needed transparency and accuracy to it.


I hope this article encourages researchers to investigate automating the manual identification and quantification process of neuronal survival after an HI attack on the neonatal brain. It is a much-needed but often overlooked part of the research on the possible treatments after the HI attack on the neonatal brain. I am glad to be part of this project.

Saheli Bhattacharya
University of Auckland

Read the Original

This page is a summary of: Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training, PLoS ONE, December 2022, PLOS,
DOI: 10.1371/journal.pone.0278874.
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