Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI

CURIALE, ARIEL H.; COLAVECCHIA, FLAVIO D.; MATO, GERMAN

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

ELSEVIER IRELAND LTD

Lugar: Amsterdam; Año: 2019 vol. 169 p. 37 - 50

0169-2607

Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN).Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These newCNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architectureand use the generalized Jaccard distance as optimization objective function.Results: The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy.Our results demonstrate a suitable accuracy for myocardial segmentation ( ∼ 0.9 Dice?s coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolicvolume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output.Conclusion: Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which arecommonly used for both diagnosis and treatment of different pathologies.Significance: This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring.