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A new interesting article has been published in Transl Stroke Res. 2019 Dec 14. doi: 10.1007/s12975-019-00754-3. and titled:

Automated Assessment of Hematoma Volume of Rodents Subjected to Experimental Intracerebral Hemorrhagic Stroke by Bayes Segmentation Approach.

Authors of this article are:

Zhang Z, Cho S, Rehni AK, Quero HN, Dave KR, Zhao W.

A summary of the article is shown below:

Simulating a clinical condition of intracerebral hemorrhage (ICH) in animals is key to research on the development and testing of diagnostic or treatment strategies for this high-mortality disease. In order to study the mechanism, pathology, and treatment for hemorrhagic stroke, various animal models have been developed. Measurement of hematoma volume is an important assessment parameter to evaluate post-ICH outcomes. However, due to tissue preservation conditions and variables in digitization, quantification of hematoma volume is usually labor intensive and sometimes even subjective. The objective of this study is to develop an automated method that can accurately and efficiently obtain unbiased cerebral hematoma volume. We developed an application (MATLAB program) that can delineate the brain slice from the background and use the Hue information in the Hue/Saturation/Value (HSV) color space to segment the hematoma region. The segmentation threshold of Hue is calculated based on the Bayes classifier theorem so that the minimum error is mathematically ensured and automated processing is enabled. To validate the developed method, we compared the outcomes from the developed method with the hemoglobin content by the spectrophotometric assay method. The results were linearly correlated with statistical significance. The method was also validated by digital phantoms with an error less than 5% compared with the ground truth from the phantoms. Hematoma volumes yielded by the automated processing and those obtained by the operator’s manual operation are highly correlated. This automated segmentation approach can be potentially used to quantify hemorrhagic outcomes in rodent stroke models in an unbiased and efficient way.

Check out the article’s website on Pubmed for more information:

[link-preview url=https://www.ncbi.nlm.nih.gov/pubmed/31836961 forceshot=true]

This article is a good source of information and a good way to become familiar with topics such as: Animal model; Bayes classifier; Color segmentation; Hematoma volume; Hemorrhage.

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