Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
المؤلف | Hassen Mohammed, Hanadi |
المؤلف | Elharrouss, Omar |
المؤلف | Ottakath, Najmath |
المؤلف | Al-Maadeed, Somaya |
المؤلف | Chowdhury, Muhammad E.H. |
المؤلف | Bouridane, Ahmed |
المؤلف | Zughaier, Susu M. |
تاريخ الإتاحة | 2023-06-21T08:20:01Z |
تاريخ النشر | 2023-04-12 |
اسم المنشور | Applied Sciences (Switzerland) |
المعرّف | http://dx.doi.org/10.3390/app13084821 |
الاقتباس | 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. |
الملخص | 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. |
راعي المشروع | This publication was supported by the Qatar University Internal Grant #QUHI-CENG-22/23-548. |
اللغة | en |
الناشر | Multidisciplinary Digital Publishing Institute (MDPI) |
الموضوع | carotid artery deep learning image segmentation intima-media thickness ultrasound imaging |
النوع | Article |
رقم العدد | 8 |
رقم المجلد | 13 |
ESSN | 2076-3417 |
الملفات في هذه التسجيلة
هذه التسجيلة تظهر في المجموعات التالية
-
علوم وهندسة الحاسب [2402 items ]
-
الهندسة الكهربائية [2649 items ]
-
أبحاث الطب [1508 items ]