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A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis

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Published on: June 2, 2015

Joint thrombus and vessel segmentation using dynamic texture likelihoods and shape prior.

Nicolas Brieu¹, Martin Groher, Jovana Serbanovic-Canic

¹Computer Aided Medical Procedures, Technische Universität München, Germany. brieu@in.tum.de

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|October 19, 2011

Summary

This study presents a new method for segmenting thrombus and vessels in microscopic images, crucial for identifying genes linked to cardiovascular diseases. The approach effectively handles low contrast and dynamic conditions in time-lapse microscopy.

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Area of Science:

Biomedical imaging
Cardiovascular research
Genetics

Background:

Accurate segmentation of thrombus and vessels in microscopic sequences is vital for understanding cardiovascular diseases.
Challenges include low contrast and dynamic conditions in time-lapse differential interference contrast (DIC) in-vivo microscopy.

Purpose of the Study:

To develop a robust probabilistic framework for the joint segmentation of thrombus and vessel regions.
To improve the identification of genes associated with cardiovascular diseases through enhanced image analysis.

Main Methods:

Utilized a probabilistic framework incorporating dynamic texture modeling.
Derived two likelihood functions to address spatial and temporal motion discrepancies.
Introduced a tubular shape prior to constrain the aortic region segmentation.

Main Results:

The proposed framework demonstrated effective joint segmentation of thrombus and vessel regions.
Quantitative experiments on microscopic sequences confirmed the approach's good performance.
Successfully addressed challenges of low contrast and dynamic environments in time-lapse DIC in-vivo microscopy.

Conclusions:

The developed probabilistic framework offers a promising solution for microscopic image segmentation in cardiovascular research.
This method can aid in the discovery of genes linked to cardiovascular diseases.
The approach shows potential for improving diagnostic and research capabilities in the field.