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Published on: February 25, 2022
Image registration and atlas-based segmentation of cardiac outflow velocity profiles
Hrvoje Kalinić1, Sven Lončarić, Maja Cikeš
1Faculty of Electrical Engineering and Computing, Department of Electronic Systems and Information Processing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia. hrvoje.kalinic@fer.hr
Insights
This study presents a novel method for segmenting aortic outflow velocity profiles from cardiac ultrasound images using a statistical atlas. The automated segmentation achieves accuracy comparable to manual methods, aiding computer-aided diagnosis of cardiovascular disease.
Area of Science:
- Medical Imaging
- Cardiology
- Computer-Aided Diagnosis
Background:
- Cardiovascular disease is a leading global cause of mortality.
- Accurate segmentation of cardiac ultrasound images is crucial for diagnosis.
- Existing methods may lack precision in extracting key cardiac parameters.
Purpose of the Study:
- To develop and validate a computer-based method for segmenting aortic outflow velocity profiles from cardiac Doppler ultrasound images.
- To improve the accuracy and efficiency of cardiac parameter extraction for disease diagnosis.
- To establish a robust segmentation technique leveraging statistical atlases.
Main Methods:
- A statistical image atlas was derived from ultrasound images of healthy volunteers.
- Image segmentation involved registration of input images to the atlas using normalized mutual information and multiresolution gradient ascent.
- Segmentation results were propagated from the atlas to the input image.
Main Results:
- The registration method demonstrated accuracy on in-silico phantoms and real volunteer data.
- Segmentation evaluation on 148 images showed excellent results.
- Automated segmentation variability was comparable to intra-observer variability for cardiac parameter extraction.
Conclusions:
- The proposed atlas-based segmentation method is effective for aortic outflow velocity profiles.
- The technique offers a valuable tool for computer-aided diagnosis of cardiovascular diseases.
- This approach facilitates reliable extraction of critical cardiac parameters from ultrasound data.
Abstract:
Cardiovascular disease is the leading cause of death worldwide and for this reason computer-based diagnosis of cardiac diseases is a very important task. In this article, a method for segmentation of aortic outflow velocity profiles from cardiac Doppler ultrasound images is presented. The proposed method is based on the statistical image atlas derived from ultrasound images of healthy volunteers. The ultrasound image segmentation is done by registration of the input image to the atlas, followed by a propagation of the segmentation result from the atlas onto the input image. In the registration process, the normalized mutual information is used as an image similarity measure, while optimization is performed using a multiresolution gradient ascent method. The registration method is evaluated using an in-silico phantom, real data from 30 volunteers, and an inverse consistency test. The segmentation method is evaluated using 59 images from healthy volunteers and 89 images from patients, and using cardiac parameters extracted from the segmented image. Experimental validation is conducted using a set of healthy volunteers and patients and has shown excellent results. Cardiac parameter segmentation evaluation showed that the variability of the automated segmentation relative to the manual is comparable to the intra-observer variability. The proposed method is useful for computer aided diagnosis and extraction of cardiac parameters.
