Image-based characterization of thrombus formation in time-lapse DIC microscopy.

Area of Science:

• Cardiovascular Research
• Biomedical Imaging
• Zebrafish Models

Background:

• Atherothrombosis and coronary artery diseases (CADs) research requires understanding thrombus formation.
• Large-scale zebrafish studies generate substantial data needing automated processing.
• In vivo DIC microscopy presents challenges like low contrast and high dynamic conditions.

Purpose of the Study:

• To develop an automated, image-based solution for extracting parameters quantifying temporal thrombus development.
• To enable large-scale analysis of thrombus formation in zebrafish for disease gene identification.

Main Methods:

• Joint segmentation of thrombotic and aortic regions over time using an image-based approach.
• Distinguishing motion patterns in image time series to overcome poor imaging conditions.
• Modeling motion patterns with dynamic textures and regularizing with shape and topological priors.

Main Results:

• Successful automated extraction of parameters quantifying thrombotic plug development.
• Demonstrated performance on synthetic and real in vivo microscopic sequences.
• Effective compensation for low contrast and high dynamic imaging conditions.

Conclusions:

• The developed system provides an effective solution for automated thrombus characterization in time-lapse DIC microscopy.
• This method facilitates large-scale genetic studies of atherothrombosis and CADs.
• Motion pattern analysis is a robust strategy for segmenting challenging in vivo microscopic data.