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Medical Image Engineering Lab
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The lab uses AI to interpret medical images and support diagnosis and treatment.
AboutAbout the lab
Building a sustainable society of healthy longevity through intelligent medical devices
The lab studies systems that extract useful information from medical images such as X-ray CT and MRI to support diagnosis and treatment.
The lab explores new theories and technologies using computational intelligence, including AI and machine learning, and aims to implement them as systems that support diagnosis and treatment.
For example, the lab studies methods that automatically detect and distinguish lesions using deep learning models, and methods that track and predict changes in tumor position and shape from X-ray fluoroscopy images.
The lab also conducts joint research with companies that develop medical devices.
Key terms
- Computational intelligence
- A general term for technologies, including AI and machine learning, that enable computers to make intelligent judgments
- Deep learning model
- An AI method that uses multilayer neural networks to learn features by itself from large amounts of data
- X-ray fluoroscopy
- An imaging method that applies X-rays to the body to show the inside of the body as continuous images
NewsLab news
View More ↗ResearchResearch themes
Medical images such as X-ray CT and MRI contain useful information that shows what is happening inside the body, which cannot be seen directly. The lab aims to build systems that support more advanced diagnosis and treatment, using new theories and technologies based on computational intelligence.
*Illustrative imageNext-generation radiation therapy systems for cancer
Aiming at safer and more effective radiation therapy for cancer, the lab studies next-generation treatment system technologies that achieve accurate irradiation control matched to each tumor.
*Illustrative imageImage-based diagnostic support for finding lesions
Using artificial intelligence and machine learning, the lab studies computer-aided diagnosis systems that detect lesions in medical images such as X-ray CT images and diagnose them.
*Illustrative imageEvaluating CT image quality and new imaging technologies
To advance diagnostic imaging with X-ray CT, the lab pursues ways to obtain better-quality images. It also studies how to appropriately evaluate new imaging technologies that are difficult to assess.
MembersFaculty
The members of the Medical Image Engineering lab consist of faculty (including faculty from other departments within the university), visiting faculty, special research fellows, academic research fellows, doctoral, master's, and undergraduate students, and research students.As of 2026
- ProfessorNoriyasu Homma
- LecturerKei Ichiji
- Project Assistant ProfessorYuwen Zeng
- Assistant ProfessorZhang Zhang
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images.
- Top image: “Computed Tomography (CT) Scan Facility in Navi Mumbai” Goleisureintl / CC BY 4.0 (cropped)
- Next-generation radiation therapy systems for cancer: “Radiation machine” liz west from Boxborough, MA / CC BY 2.0 (cropped)
- Image-based diagnostic support for finding lesions: “Computed Tomography CT Chest Imaging” Goleisureintl / CC BY 4.0 (cropped)
- Evaluating CT image quality and new imaging technologies: “CT Coronary Artery Imaging” Goleisureintl / CC BY 4.0 (cropped)
- Related lab (Medical Physics Lab): “SIEMENS Somatom AR T CT Scanner ( )” Kitmondo Marketplace / CC BY 2.0 (cropped)
- Related lab (Image Informatics Lab): “CT Brain Scan” Goleisureintl / CC BY 4.0 (cropped)
- Related lab (Diagnostic Imaging Lab): “TORNAI-SpectrumOfMedicalImaging” Martin Tornai / CC BY 4.0 (cropped)
- Related lab (Radiation Therapy Lab): “Patient prepared for radiation therapy” Rhoda Baer (Photographer) / Public domain (cropped)
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