Key result
A novel geometrically constrained Deep Neural Network using bimodal fusion improved automated segmentation of carotid ultrasound images compared to a standard network.
Why the study?
In asymptomatic carotid stenosis, assessing plaque morphology via ultrasound boundary segmentation is essential to monitor stroke risk, but requires fully automated methods.
Does a geometrically constrained deep neural network improve automated segmentation of carotid ultrasound images compared to standard networks?
Does a geometrically constrained deep neural network improve automated segmentation of carotid ultrasound images compared to standard networks?
A novel geometrically constrained deep neural network improves automated segmentation of carotid ultrasound images, potentially aiding in the monitoring of plaque morphology and stroke risk.
May aid automated carotid boundary segmentation for stroke risk monitoring; leaves open prospective clinical validation before practice change.
For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source.
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Azzopardi et al. (2020) studied Carotid stenosis (n=81,000). Geometrically constrained Deep Neural Network with bimodal fusion vs. Standard network was evaluated on Segmentation performance (DICE coefficient, modified Hausdorff Distance, sensitivity, specificity). A novel geometrically constrained Deep Neural Network using bimodal fusion improved automated segmentation of carotid ultrasound images compared to a standard network.
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