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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.
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