{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T06:26:49Z","timestamp":1771482409782,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004252","name":"Qatar University","doi-asserted-by":"publisher","award":["IRCC-2020-013"],"award-info":[{"award-number":["IRCC-2020-013"]}],"id":[{"id":"10.13039\/501100004252","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004351","name":"Sultan Qaboos University","doi-asserted-by":"publisher","award":["CL\/SQU-QU\/ENG\/20\/01"],"award-info":[{"award-number":["CL\/SQU-QU\/ENG\/20\/01"]}],"id":[{"id":"10.13039\/501100004351","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21206839","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T23:02:16Z","timestamp":1634252536000},"page":"6839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4407-5664","authenticated-orcid":false,"given":"Aisha","family":"Al-Mohannadi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0241-2899","authenticated-orcid":false,"given":"Somaya","family":"Al-Maadeed","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Elharrouss","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2730-6483","authenticated-orcid":false,"given":"Kishor Kumar","family":"Sadasivuni","sequence":"additional","affiliation":[{"name":"Center for Advanced Materials, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","first-page":"20","article-title":"A comprehensive review on heart disease prediction using data mining and machine learning techniques","volume":"4","author":"Yahaya","year":"2020","journal-title":"Am. J. Artif. Intell."},{"key":"ref_2","unstructured":"(2021, February 20). Cardiovascular Diseases. Available online: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases."},{"key":"ref_3","unstructured":"(2021, February 20). Coronary Heart Disease. 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