Sameh Oueslati | Medical Image Processing | Editorial Board Member

Editorial Board Member

Sameh Oueslati
IMT-Atlantique, Tunisia

Sameh Oueslati
Affiliation IMT-Atlantique
Country Tunisia
Scopus ID 42861895800
Documents 10
Citations 62
h-index 3
Subject Area Medical Image Processing
Event Global Tech Excellence Awards

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]

References

  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.
    https://doi.org/10.1016/j.procs.2026.03.091
  2. Elsevier. (n.d.). Scopus author details: Sameh Oueslati, Author ID 42861895800. Scopus.
    https://www.scopus.com/pages/authors/42861895800

Shuxian Lun | Image Classification | Excellence in Computer Vision Award

Prof. Shuxian Lun | Image Classification | Excellence in Computer Vision Award

Dean, School of Control Science and Engineering at Bohai University, China

Dr. Shuxian Lun is a distinguished researcher and academic affiliated with the College of Control Science and Engineering at Bohai University, China. His work spans several cutting-edge domains including artificial intelligence, image processing, fault detection, and new energy power generation technologies. With an impressive portfolio of over 90 SCI and EI-indexed publications, 22 authorized invention patents, and six published books, he has made substantial contributions to the fields of intelligent systems and automation. Dr. Lun has led four general projects and participated in a key project funded by the National Natural Science Foundation of China, demonstrating his leadership and national-level recognition. He also collaborates with researchers globally and is actively involved in consultancy and industry-linked research. As a member of IEEE and Elsevier’s academic networks, Dr. Lun maintains a strong presence in the global scientific community. His innovative mindset and multidisciplinary approach mark him as a leading figure in applied and theoretical research.

Professional Profile 

Education🎓

Dr. Shuxian Lun has built a strong educational foundation that supports his interdisciplinary research in artificial intelligence and computer vision. Although specific degree titles and universities are not detailed, his academic background has clearly equipped him with a deep understanding of control science, electrical engineering, and computational technologies. The breadth and depth of his research outputs—spanning artificial intelligence, fault detection, energy systems, and image processing—suggest rigorous graduate and postgraduate training in science and engineering. His extensive publication record, leadership in national-level projects, and innovation in applied technologies underscore a comprehensive educational journey that bridges theoretical knowledge and practical implementation. Furthermore, his successful authorship of six academic books and his role in mentoring complex R&D projects reflect his solid pedagogical foundation and academic maturity. Dr. Lun’s educational background, though not exhaustively specified, is evidently rooted in strong technical training and a commitment to continuous learning and innovation.

Professional Experience📝

Dr. Shuxian Lun has a prolific professional career as a professor and researcher at the College of Control Science and Engineering, Bohai University, China. His professional experience is marked by a strong record of academic leadership and innovation, particularly in the domains of artificial intelligence, image processing, and new energy systems. He has completed over 90 funded research projects, with two currently ongoing, and has led four general projects under the prestigious National Natural Science Foundation of China. Dr. Lun has also participated in a major key national research project and served as a consultant on five industry-oriented initiatives. His professional role involves supervising multidisciplinary research teams, developing novel technologies, and authoring books and patents. His work has culminated in the development of award-winning smart grid control systems and other technologies of national significance. These accomplishments highlight his capacity for high-impact applied research, academic mentoring, and industry collaboration.

Research Interest🔎

Dr. Shuxian Lun’s research interests lie at the intersection of artificial intelligence, image processing, fault detection, and new energy power generation technologies. He is particularly engaged in applying AI to intelligent control systems and computer vision problems, contributing to real-time monitoring, optimization, and safety in distributed energy networks. His work explores both theoretical algorithms and practical applications, including convolutional neural networks, rapid image recognition techniques, and fault-tolerant systems for smart grids. Dr. Lun also investigates the integration of AI with control engineering to enhance efficiency and reliability in power distribution systems. Furthermore, his involvement in over 90 research projects demonstrates a dynamic interest in advancing both the scientific and practical frontiers of his fields. His interdisciplinary approach enables the seamless integration of machine learning with fault diagnostics, safety assurance, and intelligent automation—areas that are pivotal for next-generation smart technologies and sustainable energy solutions.

Award and Honor🏆

Dr. Shuxian Lun has received multiple prestigious recognitions for his outstanding research and innovation. Notably, he was awarded the First Prize for Scientific and Technological Progress by the China Automation Society for the development of a “complete and practical active distribution network source network load optimization control equipment.” This award underscores the societal and industrial impact of his work in control systems and smart grids. Over the course of his career, he has presided over four general research projects and contributed to a major key project funded by the National Natural Science Foundation of China, showcasing his national-level research leadership. His innovations are further validated by the authorization of 22 invention patents and publication of 6 books. These accolades, combined with his active roles in consultancy and collaboration, reflect his influence not only within academic circles but also in shaping future-ready technologies across energy and automation sectors.

Research Skill🔬

Dr. Shuxian Lun possesses a robust and diverse set of research skills that underpin his excellence in engineering and computer science. He is proficient in the design and implementation of advanced artificial intelligence models, with a focus on computer vision, fault detection, and intelligent control systems. His technical expertise includes developing and optimizing deep learning architectures, processing high-dimensional image data, and engineering fault-tolerant systems for smart grids. He has a strong command of simulation tools, experimental design, and real-time system integration, which are crucial for applied research in control and automation. Dr. Lun also excels in academic writing, having published over 90 SCI/EI papers and 6 books, and in patent development, with 22 inventions to his name. His leadership in over 90 research projects and consultancy engagements illustrates his capacity to translate theoretical concepts into practical, impactful solutions. These capabilities make him a highly versatile and innovative researcher in multidisciplinary engineering domains.

Conclusion💡

Dr. Shuxian Lun is highly suitable for the Best Researcher Award, especially under the Computer Vision Excellence category. His research depth, innovation, national-level project leadership, and significant patent portfolio strongly reflect a top-tier research profile. With a sharper emphasis on core computer vision outcomes and citation impact in future applications, his candidacy would be even more compelling on an international stage.

Publications Top Noted✍

  • Adaptive Echo State Network with a Recursive Inverse‑Free Weight Update Algorithm
    Authors: Bowen Wang; Shuxian Lun; Ming Li; Xiaodong Lu; Tianping Tao
    Year: 2023
    Citations:

  • A New Explicit I–V Model of a Silicon Solar Cell Based on Chebyshev Polynomials
    Authors: Shu‑xian Lun; Ting‑ting Guo; Cun‑jiao Du
    Year: 2015

  • A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
    Authors: Yuping Qin; Hamid Reza Karimi; Dan Li; Shuxian Lun; Aihua Zhang
    Year: 2014

  • Preparation and Characterization of CdIn₂S₄ Wedgelike Thin Films
    Authors: Lina Zhang; Wei Zhang; Xiaodong Lu; Qiushi Wang; Xibao Yang; Libin Shi; Shuxian Lun
    Year: 2013

  • Preparation and Characterization of Cu₂ZnSnS₄ Thin Films by Solvothermal Method
    Authors: Wei Zhang; Lina Zhang; Xiaodong Lu; Qiushi Wang; Xibao Yang; Libin Shi; Shuxian Lun
    Year: 2013

  • Thermal Evaporation Synthesis and Properties of ZnO Nano/Microstructures Using Sn Reducing Agents
    Authors: Hang Lv; Xibao Yang; Xiaodong Lu; Boxin Li; Qiushi Wang; Lina Zhang; Wei Zhang; Shuxian Lun; Fan Zhang; Hongdong Li
    Year: 2013

  • Amorphous Silicon‑Assisted Self‑Catalytic Growth of FeSi Nanowires in Arc Plasma
    Authors: Qiushi Wang; Xiaodong Lu; Lina Zhang; Lv Hang; Wei Zhang; Yue Wang; Shuxian Lun
    Year: 2013

  • Design of GaAs Solar Cell Front Surface Anti‑Reflection Coating
    Authors: Tao Zhou; Xiaodong Lu; Shuxian Lun; Yuan Li; Ming Zhang; Chunxi Lu
    Year: 2013

  • Reflecting Filters Based on One Dimensional Photonic Crystal with Large Lattice Constant
    Authors: Xiaodong Lu; Shuxian Lun; Tao Zhou; Yuan Li; Chunxi Lu; Ming Zhang
    Year: 2013