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Published on: May 24, 2021
A Self-supervised Learning Based Framework for Automatic Heart Failure Classification on Cine Cardiac Magnetic
Insights
This study introduces a self-supervised learning framework (SSLHF) for classifying heart failure (HF) using cardiac MRI. SSLHF accurately distinguishes between preserved and reduced ejection fraction in HF patients.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Heart failure (HF) is a critical condition with high mortality rates.
- Accurate classification of HF based on left ventricular ejection fraction (EF) is vital for effective clinical treatment.
- Cine cardiac magnetic resonance imaging (Cine-CMR) offers more precise EF estimation than echocardiography, yet its application in HF classification remains underexplored.
Purpose of the Study:
- To propose a self-supervised learning framework (SSLHF) for automated HF classification using Cine-CMR.
- To enable the classification network to effectively learn spatial and temporal information from Cine-CMR data.
- To classify HF patients into preserved EF and reduced EF categories.
Main Methods:
- A two-stage self-supervised learning framework (SSLHF) was developed.
- Stage 1: Self-supervised image restoration using a U-Net like network to extract HF-related spatial and temporal information.
- Stage 2: HF classification using a network initialized with weights from the Stage 1 encoder.
Main Results:
- The SSLHF framework achieved an Area Under the Curve (AUC) of 0.8505.
- The framework attained an Accuracy (ACC) of 0.8208 in 5-fold cross-validation.
- The self-supervised pre-training significantly enhanced the model's ability to classify HF patients.
Conclusions:
- The proposed SSLHF framework demonstrates a promising approach for accurate HF classification using Cine-CMR.
- Self-supervised learning effectively leverages spatial and temporal information in medical images for improved diagnostic accuracy.
- SSLHF offers a valuable tool for clinical decision-making in heart failure management.
Abstract:
Heart failure (HF) is a serious syndrome, with high rates of mortality. Accurate classification of HF according to the left ventricular ejection faction (EF) plays an important role in the clinical treatment. Compared to echocardiography, cine cardiac magnetic resonance images (Cine-CMR) can estimate more accurate EF, whereas rare studies focus on the application of Cine-CMR. In this paper, a self-supervised learning framework for HF classification called SSLHF was proposed to automatically classify the HF patients into HF patients with preserved EF and HF patients with reduced EF based on Cine-CMR. In order to enable the classification network better learn the spatial and temporal information contained in the Cine-CMR, the SSLHF consists of two stages: self-supervised image restoration and HF classification. In the first stage, an image restoration proxy task was designed to help a U-Net like network mine the HF information in the spatial and temporal dimensions. In the second stage, a HF classification network whose weights were initialized by the encoder part of the U-Net like network was trained to complete the HF classification. Benefitting from the proxy task, the SSLHF achieved an AUC of 0.8505 and an ACC of 0.8208 in the 5-fold cross-validation.
Related Concept Videos
01:30Heart Failure IV: Classification and Diagnostic Evaluation
01:27Heart Failure I: Introduction
01:21Imaging Studies for Cardiovascular System IV: CMRI
01:17Imaging Studies for Cardiovascular System I:Echocardiography
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...