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Multiple two-dimensional active shape model framework for right ventricular segmentation.

Hossam El-Rewaidy¹, Ahmed S Fahmy¹, Ayman M Khalifa²

¹Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Department of Systems and Biomedical Engineering, Cairo University, Cairo University Rd, Giza, Egypt.

Magnetic Resonance Imaging

|October 23, 2021

Summary

This summary is machine-generated.

This study introduces a novel framework for segmenting the right ventricle (RV) in cardiac MRI scans. The new method significantly improves accuracy and reduces computation time compared to existing active shape models (ASM).

Keywords:

Active shape model MRI Multiple-ASM Right ventricle segmentation

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Area of Science:

Medical Imaging
Cardiovascular Imaging
Image Analysis

Background:

Right ventricle (RV) segmentation in cardiac MRI is challenging due to complex anatomy and inter-subject variability.
Active shape models (ASM) offer solutions but face trade-offs between accuracy and complexity in 2D and 3D formulations.

Purpose of the Study:

To develop an improved framework for RV segmentation in cardiac MRI using multiple 2D contours.
To enhance accuracy and efficiency in RV modeling by integrating information across cross-sectional images.

Main Methods:

Proposed a novel framework integrating multiple 2D contours for RV surface modeling.
Incorporated information from multiple cross-sectional cardiac MRI images into a unified model.
Tested the method on MRI data from 56 human subjects.

Main Results:

Achieved significantly lower segmentation error compared to conventional 2D ASM, with mean absolute distance of 2.9 ± 2 mm versus 6.6 ± 4.5 mm.
Demonstrated superior performance, especially at apical slices, reducing error by almost half.
Reduced computation time to 5 seconds, a substantial improvement over the 4 ± 1 minutes for 3D ASM.

Conclusions:

The proposed multi-contour framework offers a more accurate and computationally efficient solution for RV segmentation in cardiac MRI.
This method addresses limitations of conventional 2D and 3D ASM, particularly for complex RV anatomy.
The findings suggest a promising approach for clinical applications requiring precise RV volume and function assessment.