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1 April 2024 Utilizing a mask R-CNN for segmentation of the carotid lumen in subjects with plaque deposition
Maxwell J. Kiernan, Rashid Al Mukaddim, Carol C. Mitchell, Jenna Maybock, Stephanie M. Wilbrand, Robert J. Dempsey, Tomy Varghese
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Proceedings Volume 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 1293213 (2024) https://doi.org/10.1117/12.3005887
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
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Abstract
In patients deemed at risk of ischemic or embolic stroke, ultrasound elastography can be used to quantify plaque stability and assess a patient’s risk for plaque rupture. Localized plaque and vessel segmentation can help improve the processing time for performing high spatial resolution strain estimation. To provide this segmentation, we present a method for automatically detecting carotid lumen segmentations using a Mask R-CNN network. A previous study using this network found that a single-channel input of RF-derived B-mode images produced the most favorable results for bounding box detection and segmentation of the carotid lumen. However, prediction accuracy was low when the jugular vein was the most prominently visualized vessel in a given B-mode image. In this investigation, we present an optimized version of our Mask R-CNN network following the removal of the image resizing step and the addition of an improved validation technique. The new single-channel B-mode Mask R-CNN model produces a mean bounding box intersection over union (IoU) of 0.84 and a mean lumen segmentation IoU of 0.79 and overall producing cleaner results including those with a prominently visualized jugular vein.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Maxwell J. Kiernan, Rashid Al Mukaddim, Carol C. Mitchell, Jenna Maybock, Stephanie M. Wilbrand, Robert J. Dempsey, and Tomy Varghese "Utilizing a mask R-CNN for segmentation of the carotid lumen in subjects with plaque deposition", Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography, 1293213 (1 April 2024); https://doi.org/10.1117/12.3005887
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KEYWORDS
Image segmentation
Ultrasonography
Arteries
Atherosclerosis
Visualization
Elastography
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