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

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 theHue 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.
Source: Translational Stroke Research - Category: Neurology Source Type: research