Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
Author | Hassen Mohammed, Hanadi |
Author | Elharrouss, Omar |
Author | Ottakath, Najmath |
Author | Al-Maadeed, Somaya |
Author | Chowdhury, Muhammad E.H. |
Author | Bouridane, Ahmed |
Author | Zughaier, Susu M. |
Available date | 2023-06-21T08:20:01Z |
Publication Date | 2023-04-12 |
Publication Name | Applied Sciences (Switzerland) |
Identifier | http://dx.doi.org/10.3390/app13084821 |
Citation | Hassen 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. |
Abstract | Common 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. |
Sponsor | This publication was supported by the Qatar University Internal Grant #QUHI-CENG-22/23-548. |
Language | en |
Publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
Subject | carotid artery deep learning image segmentation intima-media thickness ultrasound imaging |
Type | Article |
Issue Number | 8 |
Volume Number | 13 |
ESSN | 2076-3417 |
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