Advanced Medical Engineering and IntelligenceSchool of Human Health Sciences

Ph.D. Professor Megumi Nakao

Our lab is engaged in research and education on artificial intelligence (AI), machine learning methods and data science for biomedical imaging, and their application to imaging, diagnosis, and clinical treatment. We aim to create medical machine intelligence for a wide range of clinical applications such as next-generation surgery and radiotherapy.

Lab Website

Research and Education

[Research] In order to address limitations of medical imaging during treatment, we are exploring a framework for deep imaging and statistical modeling technology that enable inference of high-dimensional biological information beyond observations. We focus on generality and sparsity in our organs and image measurement, and aim to directly generate information related to diagnosis and treatment. Through development of advanced information techniques, we design the automated, higher-level treatment process, and to realize sustainable medical care with low burden for both physicians and patients.

[Education] Based on the theory of machine learning and programming skills related to image processing, graphics, and computer vision, we aim to foster advanced medical professionals who can explore and solve various problems in clinical and health medicine by building and verifying theories. Through problem settings and research activities based on individual interests, we cultivate the research and development skills needed to become researchers in medical engineering and informatics and experts in advanced medical devices that will lead the future of medicine and health science.

Recent Publications

  1. M. Nakao, M. Nakamura, T. Mizowaki, T. Matsuda, Statistical deformation reconstruction using multi-organ shape features for pancreatic cancer localization, Medical Image Analysis, Vol. 67,101829, 2021.
  2. M. Nakao, K. Kobayashi, J. Tokuno, T. F. Chen-Yoshikawa, H. Date, T. Matsuda, Deformation analysis of surface and bronchial structures in intraoperative pneumothorax using deformable mesh registration, Medical Image Analysis, Vol. 73, 102181, 2021.
  3. M. Nakao, M. Nakamura, T. Matsuda, Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 259-268, 2021.
  4. M. Nakao, K. Imanishi, N. Ueda, Y. Imai, T. Kirita, T. Matsuda, Regularized three-dimensional generative adversarial nets for unsupervised metal artifact reduction in head and neck CT images, IEEE Access, Vol. 8, pp. 109453-109465, 2020.
  5. M. Nakao, J. Tokuno, T. F. Chen-Yoshikawa, H. Date, T. Matsuda, Surface Deformation Analysis of Collapsed Lungs using Model-based Shape Matching, Int. J. Computer Assisted Radiology and Surgery, 14(10), pp. 1763-1774, 2019.


Ph.D. Professor:Megumi Nakao

Lab HP:
E-mail: nakao.megumi.6x (at)


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