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

Abstract

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.

Keywords: Animal model; Bayes classifier; Color segmentation; Hematoma volume; Hemorrhage.

Fig. 1

Examples of digitized brain sections of rats subjected to the hemorrhagic stroke described above. Top row (a-c): Original sections show the brain tissue and surrounding background. Middle row (d-f): Edge detection based on image gradients forms the boundary of the section. Bottom row (g-i): Extracted sections (delineated with green lines) are available for color segmentation.

Fig. 2

Images displayed in R (a-c), G (e-g), B (i-k), and intensity (m-o) channels from top row to bottom row, respectively. Right column (d, h, l, p) shows the histogram corresponding to each channel. Though the bi-peak distributions can be recognized, identification of the small peak that represents hematoma pixels is extremely difficult. The small peaks are either too small or almost merged into the major peaks without clear separation points

Fig. 3

The Hue histogram consists of two overlapped peaks, one peak representing pixels belonging to the normal tissue, and the other peak representing pixels belonging to the hematoma tissue. It is reasonable to assume that distribution of normal brain tissue and the distribution of hematoma tissue are independent. These two peaks can be fitted into two Gaussian distributions (with R² = 0.9949 for hematoma peak, and R² = 0.9713 for normal brain tissue peak), and the overall histogram can be seen as a linear combination of these two Gaussian distributions. Though the “valley” between two distributions is not deep to zero, i.e., completely separate, we can seek available theory to solve the problem.

Fig. 4

The top row (a-c) shows the original sections at different bregma levels. The second to the forth row (d-l) of the images represent three different manual segmentations by using Adobe® Photoshop®. Due to a different sample of hematoma regions each time, delineation of the hematoma region was determined by a different threshold. The bottom row (m-o) shows the results from the proposed automated color segmentation processing procedure.

Fig. 5

Correlation between hemoglobin content and estimated hematoma volume by the manual method and automated method. Data point (orange triangle) shows a linear correlation between hemoglobin content and volume obtained by manual segmentation method (R² = 0.40, n = 10, p < 0.048). Data point (blue circle) shows a linear correlation between hemoglobin content and volume by automated segmentation method (R² = 0.46, n = 10, p < 0.031).

Fig. 6

(A) Rat model: Correlation between manually acquired hematoma area and automatically acquired hematoma area shows that the proposed method is consistent with the manual processing (slope = 1.05, R² = 0.94, p < 0.0001). (B) Mouse model: Correlation between manually acquired hematoma area and automatically acquired hematoma area shows that the manual processing slightly overestimated hematoma area (slope = 0.70, R² = 0.80, p < 0.0001).

Fig. 7

Simulated phantoms and automated segmentation results at various noise level settings. Images on top row (a–c) are simulated phantoms, and the corresponding segmented images (d–f) are the automated processed results. The noise level increased from one standard deviation to triple standard deviation from left to right