Segmentation of Soft atherosclerotic plaques using active contour models
Muhammad Moazzam Jawaid

TL;DR
This paper presents a semi-automatic framework for segmenting soft atherosclerotic plaques in CTA images, utilizing active contour models and mathematical modeling of contrast agent diffusion to improve detection accuracy.
Contribution
It introduces a novel segmentation approach combining seed point detection, contrast agent modeling, and active contours to identify vulnerable plaques with minimal user intervention.
Findings
Successful automatic seed point detection based on artery structure.
Effective modeling of contrast agent diffusion in CTA data.
Framework ready for future soft plaque evaluation.
Abstract
Detection of non-calcified plaques in the coronary tree is a challenging problem due to the nature of comprising substances. Hard plaques are easily discernible in CTA data cloud due to apparent bright behaviour, therefore many approaches have been proposed for automatic segmentation of calcified plaques. In contrast soft plaques show very small difference in intensity with respect to surrounding heart tissues & blood voxels. This similarity in intensity makes the isolation and detection of soft plaques very difficult. This work aims to develop framework for segmentation of vulnerable plaques with minimal user dependency. In first step automatic seed point has been established based on the fact that coronary artery behaves as tubular structure through axial slices. In the following step the behaviour of contrast agent has been modelled mathematically to reflect the dye diffusion in…
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Taxonomy
TopicsCoronary Interventions and Diagnostics · Medical Image Segmentation Techniques · Cerebrovascular and Carotid Artery Diseases
