Editorial Board Member
Sameh Oueslati
IMT-Atlantique, Tunisia
Sameh Oueslati is a researcher affiliated with IMT-Atlantique whose scholarly profile is associated with medical image processing and deep-learning-based image segmentation. His documented research includes comparative work on convolutional and fully convolutional neural-network approaches for short-axis left-ventricle segmentation in cardiac cine magnetic resonance imaging. [1] His indexed Scopus profile records 10 documents, 62 citations, and an h-index of 3. [2]
Abstract
Sameh Oueslati is affiliated with IMT-Atlantique and works in medical image processing, with research interests reflected in deep-learning approaches for cardiac magnetic resonance image analysis. His documented publication examines CNN and FCN approaches for short-axis left-ventricle segmentation in cardiac cine MR sequences, highlighting automated segmentation as a means of supporting efficient and reproducible cardiac image analysis. [1] His Scopus profile records 10 documents, 62 citations, and an h-index of 3. [2]
Keywords
- Medical Image Processing
- Cardiac Magnetic Resonance Imaging
- Left-Ventricle Segmentation
- Deep Learning
- Convolutional Neural Networks
- Fully Convolutional Networks
Introduction
Medical image processing combines computational methods with clinical imaging to support quantitative analysis and interpretation. In cardiac MRI, automated segmentation of the left ventricle can assist assessment of cardiac structures and reduce dependence on time-intensive manual delineation. Oueslati’s documented research addresses this problem through comparative evaluation of CNN and FCN approaches. [1]
Research Profile
Oueslati’s research profile is centered on medical image processing, particularly deep-learning methods for segmentation of cardiac MRI images. The available publication record identifies work involving short-axis cardiac cine MR sequences and automated left-ventricle delineation. Scopus records 10 documents, 62 citations, and an h-index of 3 for the indexed author profile. [2]
Research Contributions
A documented contribution by Oueslati is the comparative investigation of CNN and FCN performance for short-axis left-ventricle segmentation in cardiac cine MR sequences. The study evaluates automated segmentation methods and reports that the investigated FCN approach was particularly suitable for the task, demonstrating the relevance of deep-learning architectures to cardiac image analysis. [1]
Publications
The identified publication by Oueslati and Basel Solaiman examines CNN and FCN performance for short-axis left-ventricle segmentation in cardiac cine MR sequences. It appears in Procedia Computer Science, volume 278, with pages 1120–1127. The article addresses image segmentation, deep learning, cardiac cine MRI, CNNs, and FCNs within a medical imaging context. [1]
- Oueslati, S., & Solaiman, B. (2026). A comparative study of CNN and FCN performance for short-axis left ventricle segmentation in cardiac cine MR sequences. Procedia Computer Science, 278, 1120–1127.
Research Impact
The available indexed record indicates measurable scholarly visibility, with 62 citations and an h-index of 3 reported for the Scopus author profile. [2] The documented cardiac MRI study contributes to the broader development of automated medical image segmentation by examining alternative neural-network architectures for left-ventricle delineation and computational support of cardiac image analysis. [1]
Award Suitability
Oueslati’s documented work is relevant to recognition in medical image processing because it applies deep-learning techniques to a clinically meaningful cardiac imaging problem. The comparative analysis of CNN and FCN architectures demonstrates engagement with automated segmentation methodology, while the indexed publication and citation record provide identifiable scholarly evidence for evaluating research activity and contribution. [1] [2]
Conclusion
Sameh Oueslati’s documented research profile reflects activity in medical image processing, particularly automated cardiac MRI segmentation using deep-learning architectures. His identified publication provides evidence of comparative methodological research involving CNN and FCN approaches, while the Scopus profile indicates an established indexed record with 10 documents, 62 citations, and an h-index of 3. [1] [2]
External Links
References
- Oueslati, S., & Solaiman, B. (2026). A comparative study of CNN and FCN performance for short-axis left ventricle segmentation in cardiac cine MR sequences. Procedia Computer Science, 278, 1120–1127.
https://doi.org/10.1016/j.procs.2026.03.091
- Elsevier. (n.d.). Scopus author details: Sameh Oueslati, Author ID 42861895800. Scopus.
https://www.scopus.com/pages/authors/42861895800