Mr. Nikolaos Bouzianis | Medical Physics | Best Researcher Award

Mr. Nikolaos Bouzianis | Medical Physics | Best Researcher Award

Medical Physicist, P.G.N. Alexandroupolis, Greece.

Nikolaos Bouzianis is a dedicated Medical Physicist and Ph.D. candidate at the Democritus University of Thrace, School of Medicine. With a strong academic foundation in medical physics and hands-on clinical experience in nuclear medicine, Nikolaos has continuously demonstrated a passion for applying advanced technologies—particularly artificial intelligence—to optimize diagnostic imaging and patient care. He holds national certifications as a Medical Physics Expert and Radiation Protection Expert, highlighting his commitment to excellence and safety in medical environments.

Publication Profile

ORCID

🎓 Education Background

Nikolaos is currently pursuing a Ph.D. in Medical Physics (2023–Present) at the Democritus University of Thrace, where his research focuses on leveraging AI algorithms to enhance practices in nuclear medicine. He earned his Master’s degree in Medical Physics (2015–2017) from the University of Patras with an “Excellent” distinction for his thesis on HDL image classification via application development. He also holds a Bachelor’s degree in Physics (2007–2015) from the same university, where he specialized in electronics, computers, and signal processing.

🧪 Professional Experience

Since January 2021, Nikolaos has been working as a Medical Physicist at the University General Hospital of Alexandroupolis, applying his knowledge in clinical settings. He also completed a residency as a Medical Physicist at the University General Hospital of Patras from November 2018 to October 2019. His roles have equipped him with practical exposure to diagnostic imaging, radiation safety, and computational medical physics applications.

🏅 Awards and Honors

Nikolaos holds multiple professional certifications, including a license to practice as a Hospital Physicist in both ionizing and non-ionizing radiation fields. He is recognized as a Certified Medical Physics Expert and Radiation Protection Expert. These honors reflect his high competency level and his respected status in the field of medical physics.

🔬 Research Focus

Nikolaos’s primary research interests lie at the intersection of artificial intelligence and nuclear medicine. His Ph.D. work explores how AI algorithms can optimize existing medical practices, focusing especially on enhancing the quality of scintigraphic imaging while reducing radiation dose. His work exemplifies innovation in digital medical technology, particularly through the use of convolutional autoencoders and advanced image processing techniques.

🔚 Conclusion

With a blend of academic rigor, clinical experience, and a tech-forward mindset, Nikolaos Bouzianis stands out as a promising figure in the field of medical physics. His contributions to AI-based solutions in nuclear medicine aim to redefine diagnostic standards and improve patient outcomes, positioning him as a valuable asset to both research and healthcare communities.

📚 Publication Top Notes

🔹 Title: Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising
🔹 Journal: J. Imaging
🔹 Year: 2025
🔹 Volume/Issue: 11(6), Article 197
🔹 Cited by: 2 articles (as per current indexing)

Doohee Lee | Medical imaging | Computer Vision Contribution Award

Mr. Doohee Lee | Medical imaging | Computer Vision Contribution Award

ZIOVISION Co., Ltd., South Korea

Doohee Lee is the Chief Operating Officer (COO) at ZIOVISION Co., Ltd., where he leads advancements in AI-driven medical imaging solutions. With over a decade of experience in the medical AI field, he has collaborated with notable institutions such as Seoul National University Hospital and MEDICALIP Co., Ltd. He has published over 15 peer-reviewed articles and holds multiple U.S. and international patents in medical imaging and AI technologies. His innovative contributions in healthcare aim to revolutionize diagnostics and improve patient outcomes through advanced imaging technologies. 🔬💡

Publication Profile

Education:

Doohee Lee holds a B.S. and M.S. in Computer Science Engineering and is currently a Ph.D. candidate at Kangwon National University. His academic background has equipped him with a strong foundation in deep learning, medical image analysis, and AI applications in healthcare. 🎓📚

Experience:

With extensive experience in both academia and industry, Doohee Lee has contributed significantly to the field of medical AI. As the COO of ZIOVISION, he leads R&D teams in the development of cutting-edge medical imaging technologies. His previous roles at MEDICALIP Co., Ltd. and Seoul National University Hospital have allowed him to advance research projects and industry collaborations in the AI healthcare space. 💼🧑‍💻

Awards and Honors:

Doohee Lee’s groundbreaking work has earned him numerous accolades, including recognition in AI-driven medical imaging advancements. His efforts have led to significant developments in the field of medical diagnostics, especially in AI-based image segmentation and automated analysis. 🏆👏

Research Focus:

Doohee Lee specializes in AI-driven medical image analysis, focusing on deep learning-based segmentation, 3D image analysis, and clinical AI applications. His ongoing research includes automated tumor segmentation, sepsis mortality prediction, and osteoporosis grading via CT. He has also worked on developing AI models for predictive healthcare solutions. 🧠💻

Conclusion:

Doohee Lee’s expertise in medical AI and his leadership at ZIOVISION continue to drive innovation in healthcare. With a strong focus on utilizing AI to improve diagnostic accuracy and patient outcomes, he is at the forefront of technological advancements in the medical imaging sector. His contributions are shaping the future of AI-powered healthcare solutions. 🌐💪

Publications:

A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging (2025) – Applied Sciences
DOI: 10.3390/app15031539
Cited by: 10 citations 📑

Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas  (2024) – Computer Methods and Programs in Biomedicine
DOI: 10.1016/j.cmpb.2024.108379
Cited by: 5 citations 📑

Deep Learning-Based Dual-Stage Model for Accurate Nasogastric Tube Positioning in Chest Radiographs (2024) – SSRN
DOI: 10.2139/ssrn.4965848
Cited by: 3 citations 📑

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study (2024) – Journal of Korean Medical Science
DOI: 10.3346/jkms.2024.39.e53
Cited by: 15 citations 📑

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning (2022) – Journal of Magnetic Resonance Imaging
DOI: 10.1002/jmri.28332
Cited by: 50 citations 📑

Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results. (2022) – Acta radiologica (Stockholm, Sweden: 1987)
DOI: 10.1177/02841851221100318
Cited by: 20 citations 📑

Clinical application of patient-specific 3D printing brain tumor model production system for neurosurgery (2021) – Scientific Reports
DOI: 10.1038/s41598-021-86546-y
Cited by: 30 citations 📑