This manuscript describes an image-based scheme for automatic segmentation and measurement of thrombosis. Biologists inject drugs that can cause thrombosis in mice and use a Confocal Laser Scanning Microscope (CLSM) to observe changes in blood vessels to understand the mechanism of thrombosis. However, it is difficult to segment the thrombus region in CLSM images because the thrombus region is very similar to the background. Therefore, computer vision-based methods are used to analyze thrombosis and assist biologists. A previous method used the difference between a preset reference frame (fixed frame) and a frame (current frame) to locate the thrombus region. However, this method did not take into account that the thrombus always grows inside the blood vessels, resulting in mis-segmented thrombus regions. Therefore, we use the anatomical structure relationship of the mouse to increase the accuracy of thrombus segmentation. We use the difference between the current frame and a reference frame to segment the thrombus region. The blood vessel, which is a representative anatomical structure in the CLSM image, is found using Otsu-based thresholding and is used to remove the false positive thrombus regions. The remaining thrombus region is used to calculate the size, the centroid coordinate of the thrombus, and the growth rate of the thrombus region. We created the ground truth of the thrombus regions to validate the proposed method. Experimental results showed that the DICE value of the proposed method was 0.76 ± 0.13.
In this paper, we propose a scheme that includes automated extraction of thrombus regions and quantitative analysis of thrombosis in confocal laser scanning microscope (CLSM) blood flow image sequence. Making thrombosis model in animal models play an important role in the development of antithrombotic drugs and ascertaining thrombosis mechanisms. Making thrombosis model in cerebral cortex of mice is usually observed using a CLSM in the fluorescence mode. However, some small changes of thrombus regions are not easily observed in CLSM blood flow image sequences. In addition, it is not easy for researchers to quantitatively analyze the degree of thrombosis. Therefore, we propose a scheme to achieve automatic thrombosis region extraction and quantitative analysis. In which, our thrombosis region extraction method uses analysis of changing pattern of thrombosis regions in CLSM blood flow image sequence. Experimental results showed that our scheme can help biological researchers observe and analyze the changes of thrombosis in animal models and reduced the use of fluorescent thrombus markers.
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