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Imaging Studies for Cardiovascular System V: CT

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Cardiac Multi-detector CT Segmentation Based on Multiscale Directional Edge Detector and 3D Level Set.

Sofia Antunes^1,2, Antonio Esposito^3,4, Anna Palmisano^3,4

¹Experimental Imaging Center, San Raffaele Scientific Institute, via olgettina 58, 20132, Milan, Italy. sofigantunes@gmail.com.

Annals of Biomedical Engineering

|August 31, 2015

Summary

This study introduces an automatic method for segmenting heart structures from CT scans, significantly improving accuracy and reducing manual effort in cardiac analysis.

Keywords:

Cardiac segmentation Edge detection Filter bank Level set

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Area of Science:

Medical Imaging
Cardiovascular Imaging
Image Segmentation

Background:

Accurate cardiac surface extraction from multi-detector computed tomographic (MDCT) data is crucial for cardiac analysis and image guidance.
Existing segmentation methods often require time-consuming manual corrections.

Purpose of the Study:

To develop a fully automatic segmentation technique for extracting the right ventricle, left ventricular endocardium, and epicardium from MDCT images.
To improve the efficiency and accuracy of cardiac structure segmentation.

Main Methods:

A 3D level set surface evolution approach was employed.
A novel stopping function utilizing a multiscale directional second derivative Gaussian filter was integrated to precisely identify structure boundaries.
The method was validated on 18 MDCT volumes against manual segmentation by expert radiologists.

Main Results:

The automatic segmentation achieved a surface-to-surface mean error below 0.5 mm.
Over 80% of the surface distance errors were less than 1 mm.
The proposed method demonstrated improved accuracy compared to previous approaches, with an 8-20% increase in correctly segmented areas (errors < 1 mm).

Conclusions:

The developed automatic segmentation technique is accurate and effective for segmenting ventricular cavities and myocardium from MDCT images.
This approach offers a significant improvement over existing methods, reducing the need for manual intervention.