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المؤلفHassen Mohammed, Hanadi
المؤلفElharrouss, Omar
المؤلفOttakath, Najmath
المؤلفAl-Maadeed, Somaya
المؤلفChowdhury, Muhammad E.H.
المؤلفBouridane, Ahmed
المؤلفZughaier, Susu M.
تاريخ الإتاحة2023-06-21T08:20:01Z
تاريخ النشر2023-04-12
اسم المنشورApplied Sciences (Switzerland)
المعرّفhttp://dx.doi.org/10.3390/app13084821
الاقتباسHassen Mohammed, H., Elharrouss, O., Ottakath, N., Al-Maadeed, S., Chowdhury, M. E., Bouridane, A., & Zughaier, S. M. (2023). Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models. Applied Sciences, 13(8), 4821.
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85156098299&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/44645
الملخصCommon carotid intima-media thickness (CIMT) is a common measure of atherosclerosis, often assessed through carotid ultrasound images. However, the use of deep learning methods for medical image analysis, segmentation and CIMT measurement in these images has not been extensively explored. This study aims to evaluate the performance of four recent deep learning models, including a convolutional neural network (CNN), a self-organizing operational neural network (self-ONN), a transformer-based network and a pixel difference convolution-based network, in segmenting the intima-media complex (IMC) using the CUBS dataset, which includes ultrasound images acquired from both sides of the neck of 1088 participants. The results show that the self-ONN model outperforms the conventional CNN-based model, while the pixel difference- and transformer-based models achieve the best segmentation performance.
راعي المشروعThis publication was supported by the Qatar University Internal Grant #QUHI-CENG-22/23-548.
اللغةen
الناشرMultidisciplinary Digital Publishing Institute (MDPI)
الموضوعcarotid artery
deep learning
image segmentation
intima-media thickness
ultrasound imaging
العنوانUltrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
النوعArticle
رقم العدد8
رقم المجلد13
ESSN2076-3417
dc.accessType Open Access


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