Title | Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Baskaran L, Al'Aref SJ, Maliakal G, Lee BC, Xu Z, Choi JW, Lee S-E, Sung JMin, Lin FY, Dunham S, Mosadegh B, Kim Y-J, Gottlieb I, Lee BKwon, Chun EJu, Cademartiri F, Maffei E, Marques H, Shin S, Choi JHyun, Chinnaiyan K, Hadamitzky M, Conte E, Andreini D, Pontone G, Budoff MJ, Leipsic JA, Raff GL, Virmani R, Samady H, Stone PH, Berman DS, Narula J, Bax JJ, Chang H-J, Min JK, Shaw LJ |
Journal | PLoS One |
Volume | 15 |
Issue | 5 |
Pagination | e0232573 |
Date Published | 2020 |
ISSN | 1932-6203 |
Keywords | Aged, Computed Tomography Angiography, Coronary Vessels, Deep Learning, Female, Heart, Heart Atria, Heart Ventricles, Humans, Male, Middle Aged |
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. |
DOI | 10.1371/journal.pone.0232573 |
Alternate Journal | PLoS One |
PubMed ID | 32374784 |
PubMed Central ID | PMC7202628 |
Related Institute:
Dalio Institute of Cardiovascular Imaging (Dalio ICI)