Deploying deep learning approaches to left ventricular non-compaction measurement
| Autor: | Jesús M. Rodríguez-de-Vera, José M. García, Gregorio Bernabé, Josefa González-Carrillo |
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| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Xeon Computer science business.industry Deep learning Population Cardiomyopathy Hypertrophic cardiomyopathy Inference Pattern recognition Mri studies medicine.disease Convolutional neural network Theoretical Computer Science medicine.anatomical_structure Hardware and Architecture Ventricle medicine Artificial intelligence education business Software Information Systems |
| Zdroj: | The Journal of Supercomputing. 77:10138-10151 |
| ISSN: | 1573-0484 0920-8542 |
| DOI: | 10.1007/s11227-021-03664-0 |
| Popis: | Left ventricular non-compaction (LVNC) is a rare cardiomyopathy characterized by abnormal trabeculations in the left ventricle cavity. Although traditional computer vision approaches exist for LVNC diagnosis, deep learning-based tools could not be found in the literature. In this paper, a first approach using convolutional neural networks (CNNs) is presented. Four CNNs are trained and tuned to automatically segment the compacted and trabecular areas of the left ventricle for a population of patients diagnosed with hypertrophic cardiomyopathy. We have evaluated them in the learning phase with an NVIDIA Turing GPU and 2 competitive Xeon CPUs. Inference results confirm that deep learning-based approaches can achieve excellent results in the diagnosis and measurement of LVNC. The final proposal enables real-time analysis of 15-slice MRI studies on both GPU and CPU, obtaining noticeable speed-ups with regard to manual, semi-automatic and fully automatic approaches. Additionally, a subjective evaluation of the output images with the identified zones is performed by expert cardiologists, with a perfect visual agreement for all the slices, outperforming already existing automatic tools. |
| Databáze: | OpenAIRE |
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