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Published on: May 24, 2021
Cardiac Anomaly Detection from Cine MRI Images using Physiological Features and Random Forest Classifier
Insights
This study introduces an automated system for diagnosing cardiovascular diseases using cardiac MRI. The novel approach achieves high accuracy in identifying heart abnormalities, improving diagnostic speed and reliability.
Area of Science:
- Medical Imaging
- Cardiovascular Diseases
- Artificial Intelligence
Background:
- Current cardiovascular disease (CVD) diagnosis using cine MRI faces limitations in accuracy for borderline cases.
- Manual visualization of 3D/4D MR data is time-consuming and requires expert interpretation.
Purpose of the Study:
- To develop an end-to-end automated computer-aided diagnosis (CAD) system for cardiovascular diseases.
- To improve the accuracy and efficiency of cardiac diagnosis using cine MRI.
Main Methods:
- Automated segmentation of critical heart substructures from cine MRI data.
- Calculation of novel domain-specific physiological features from segmented regions.
- Classification of cardiac anomalies using a random forest classifier.
Main Results:
- Achieved very high accuracy on the Automated Cardiac Diagnosis challenge (ACDC) dataset.
- Demonstrated generalizability with over 90% accuracy on the multi-center M&Ms-2 dataset.
Conclusions:
- The developed end-to-end automated CAD system offers a significant improvement over conventional methods for diagnosing CVD.
- The system shows strong potential for accurate, efficient, and generalizable cardiac diagnosis across diverse datasets.
Abstract:
Computer-aided diagnosis (CAD) with cine MRI is a foremost research topic to enable improved, faster, and more accurate diagnosis of cardiovascular diseases (CVD). However, current approaches that use manual visualization or conventional clinical indices can lack accuracy for borderline cases. Also, manual visualization of 3D/4D MR data is time-consuming and expert-dependent. We try to simplify this process by creating an end-to-end automated CAD system that segments the critical substructures of the heart. The new domain-related physiological features are then calculated from the segmented regions. These features are fed to a random forest classifier that identifies the anomaly. We have obtained a very high accuracy when testing this end-to-end approach on the Automated Cardiac Diagnosis challenge (ACDC) dataset (4 pathologies, 1 normal). To prove the generalizability of the method we have blind-tested this approach on M&Ms-2 dataset which is a multi-center, multi-vendor, and multi-disease dataset with better than 90% accuracy.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...