Automatic Left Ventricle Quantification in Cardiac MRI via Hierarchical Refinement of High-Level Features by a Salient Perceptual Grouping Model

Abstract

An accurate segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) provides reliable cardiac indexes such as the ventricular volume, the ejection fraction or regional wall thicknesses (RWT). This paper introduces an automated method to compute such indexes in 2D MRI slices from a semantic segmentation obtained in two steps. A first coarse segmentation is obtained by applying an encoder-decoder neural network architecture that assigns a probability value to each pixel. Afterwards, this segmentation is refined by a spatio-temporal saliency analysis. The method was evaluated in MR sequences of 175 subjects divided in two groups: training (145 subjects) and test (30 subjects). For the training data set, using a K-cross validation setup, the method achieves an average Pearson correlation coefficient of 0.98, 0.92, 0.95 and 0.75 with the set of indexes LV cavity, myocardium areas, cavity dimensions and region wall thicknesses, respectively, while classification of the cardiac phase yielded a rate of $$10.01\%$$. For the same set of indexes, evaluated in the test dataset, an average Pearson correlation coefficient of 0.98, 0.87, 0.97 and 0.66 was obtained. Additionally, the cardiac phase classification error rate was $$9\%$$. The method provides a reliable LV segmentation and quantification of cardiac indexes.