Automatic Abdominal Aortic Aneurysm Detection from Ultrasound Imaging using Deep Learning
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2021-12-01
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en
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Abstract
An abdominal aortic aneurysm (AAA) is an enlargement of the abdominal
aorta. If an AAA ruptures, it leads to death in 48.5% to 81% of the cases.
Detection and monitoring of AAAs is therefore vital and is currently performed
by a trained sonographer at a hospital. A general practitioner is not
able to perform an ultrasound, since it requires months of training. In this
study, we present a method based on deep learning to automatically detect
an aorta from ultrasound (US) imaging and to automatically measure the
aortic diameter, so untrained people would be able to measure the aortic
diameter without extensive training. The method consists of two steps. In
the rst step, a deep learning model with a U-Net architecture segments the
aorta for each acquired US frame. In the second step, connected-component
labeling (CCL) is used to nd the segmented aorta, and a direct least-squares
ellipse t is performed to measure the aortic diameter. Data from 100 patients
was acquired. A handheld US device was used to make an axial sweep
of 7 seconds from the xiphoid process up until the umbilicus. Data of 80
patients was used to train the algorithms. 20 patients were used as a test set
which showed a median Dice of 0.88 (IQR = 0.78 - 0.92). The segmentation
model was included into a smartphone application, which was used to acquire
data from 44 additional patients which also received a computed tomography
(CT) as ground truth. The results show that the CT-US maximum diameter
di erences had a median of 6.0 mm (IQR = 4.0 - 9.6 mm) and 73.8% of the
measurements fell within within the clinically acceptable limits of agreement
of 5 mm.
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