Abstract:
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 new CNNs 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 architecture and 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-systolic volume, 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 are commonly 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.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Curiale AH,Colavecchia FD,Mato Gdoi
10.1016/j.cmpb.2018.12.002subject
Has Abstractpub_date
2019-02-01 00:00:00pages
37-50eissn
0169-2607issn
1872-7565pii
S0169-2607(18)31169-6journal_volume
169pub_type
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journal_title:Computer methods and programs in biomedicine
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