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Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
- Source :
- PLoS ONE, 15(5). PUBLIC LIBRARY SCIENCE, PLoS ONE, PloS one, vol 15, iss 5, PLoS ONE, Vol 15, Iss 5, p e0232573 (2020)
- Publication Year :
- 2020
-
Abstract
- OBJECTIVES: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS: Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS: The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS: An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.
- Subjects :
- Male
Computed Tomography Angiography
Settore MED/11 - Malattie dell'Apparato Cardiovascolare
Cardiovascular Medicine
030204 cardiovascular system & hematology
Cardiovascular
Diagnostic Radiology
030218 nuclear medicine & medical imaging
Machine Learning
0302 clinical medicine
Medicine and Health Sciences
Segmentation
Cardiovascular Imaging
Pulmonary Arteries
Tomography
Aorta
Coronary Arteries
Multidisciplinary
medicine.diagnostic_test
Radiology and Imaging
Angiography
Heart
Arteries
Middle Aged
Coronary Vessels
Heart Disease
medicine.anatomical_structure
Descending aorta
Biomedical Imaging
Medicine
Female
Anatomy
Research Article
Computer and Information Sciences
General Science & Technology
Imaging Techniques
Science
Heart Ventricles
Neuroimaging
Research and Analysis Methods
Veins
03 medical and health sciences
Deep Learning
Artificial Intelligence
Diagnostic Medicine
medicine.artery
medicine
Humans
Heart Atria
Coronary sinus
Aged
business.industry
Deep learning
Biology and Life Sciences
Computed Axial Tomography
Coronary arteries
Pulmonary artery
Cardiovascular Anatomy
Blood Vessels
Artificial intelligence
Nuclear medicine
business
Neuroscience
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, 15(5). PUBLIC LIBRARY SCIENCE, PLoS ONE, PloS one, vol 15, iss 5, PLoS ONE, Vol 15, Iss 5, p e0232573 (2020)
- Accession number :
- edsair.doi.dedup.....02cfbbc26356b67ec0b2cfc952b45a6c