Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
This work addresses the need for more reliable cardiac parameter estimation in medical imaging, though it appears incremental as it builds on existing CNN and U-Net methods.
The paper tackled the problem of accurate left ventricle quantification for cardiovascular disease diagnosis by developing an ensemble learning framework that combines direct estimation and segmentation modules, achieving superior performance over base modules on the LVQuan18 dataset.
Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The framework combines two 1st-level modules: direct estimation module and a segmentation module. The direct estimation module utilizes Convolutional Neural Network (CNN) to achieve end-to-end quantification. The CNN is trained by taking 2D cardiac images as input and cardiac parameters as output. The segmentation module utilizes a U-Net architecture for obtaining pixel-wise prediction of the epicardium and endocardium of LV from the background. The binary U-Net output is then analyzed by a separate CNN for estimating the cardiac parameters. We then employ linear regression between the 1st-level predictor and ground truth to learn a 2nd-level predictor that ensembles the results from 1st-level modules for the final estimation. Preliminary results by testing the proposed framework on the LVQuan18 dataset show superior performance of the ensemble learning model over the two base modules.