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Article

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

1
Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar
2
Center for Advanced Materials, Qatar University, Doha P.O. Box 2713, Qatar
*
Author to whom correspondence should be addressed.
Academic Editors: Ayman El-baz, Guruprasad A. Giridharan, Ahmed Shalaby, Ali H. Mahmoud and Mohammed Ghazal
Sensors 2021, 21(20), 6839; https://doi.org/10.3390/s21206839
Received: 31 August 2021 / Revised: 26 September 2021 / Accepted: 9 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Computer Aided Diagnosis Sensors)
Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work. View Full-Text
Keywords: carotid intima-media thickness; IMT; CCA; segmentation; deep learning; encoder-decoder model carotid intima-media thickness; IMT; CCA; segmentation; deep learning; encoder-decoder model
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MDPI and ACS Style

Al-Mohannadi, A.; Al-Maadeed, S.; Elharrouss, O.; Sadasivuni, K.K. Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement. Sensors 2021, 21, 6839. https://doi.org/10.3390/s21206839

AMA Style

Al-Mohannadi A, Al-Maadeed S, Elharrouss O, Sadasivuni KK. Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement. Sensors. 2021; 21(20):6839. https://doi.org/10.3390/s21206839

Chicago/Turabian Style

Al-Mohannadi, Aisha, Somaya Al-Maadeed, Omar Elharrouss, and Kishor K. Sadasivuni 2021. "Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement" Sensors 21, no. 20: 6839. https://doi.org/10.3390/s21206839

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