  This study applies convolutional neural network (CNN)-based automatic
segmentation and distensibility measurement of the ascending and descending
aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI)
within the large MESA cohort with subsequent assessment on an external cohort
of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the
ascending and descending aorta at the pulmonary artery bifurcation from the
MESA study were included. Train, validation, and internal test sets consisted
of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies
(8069 images), respectively. An external test set of TAAs consisted of 37
studies (3224 images). A U-Net based CNN was constructed, and performance was
evaluated utilizing dice coefficient (for segmentation) and concordance
correlation coefficients (CCC) of aortic geometric parameters by comparing to
manual segmentation and parameter estimation. Dice coefficients for aorta
segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the
internal and external test of TAAs, respectively. CCC for comparison of manual
and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95,
respectively, on the internal test set and 0.997 and 0.995, respectively, for
the external test. CCCs for maximum and minimum descending aortic areas were
0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93,
respectively, on the external test set. We successfully developed and validated
a U-Net based ascending and descending aortic segmentation and distensibility
quantification model in a large multi-ethnic database and in an external cohort
of TAA patients.
