Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image...

Area of Science:

• Medical Imaging
• Computational Anatomy
• Cardiovascular Research

Background:

• Accurate quantification of vessel morphology is crucial for diagnosing and treating cardiovascular diseases.
• Existing methods may struggle with diverse or pathological vessel shapes.

Purpose of the Study:

• To introduce a novel joint segmentation and registration approach for quantifying aortic arch morphology.
• To combine the robustness of model-based segmentation with the accuracy of elastic registration.

Main Methods:

• A new approach integrating 3D model-based segmentation and elastic image registration was developed.
• The method was evaluated using synthetic 3D images, phantom data, and clinical 3D CTA images with pathologies.

Main Results:

• The combined approach demonstrated robustness across a wide range of vessel shapes, including pathological cases.
• Quantitative comparisons with previous methods were performed to validate performance.

Conclusions:

• The proposed joint segmentation and registration method offers an accurate and robust solution for aortic arch morphology quantification.
• This technique has potential applications in the diagnosis and treatment planning for cardiovascular diseases.