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Intelligent Medical Engineering Lab
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A lab that uses AI to infer what cannot be seen inside the body, for use in surgery and radiation therapy.
AboutAbout the lab
Inferring the inside of the body from partial observations.
The Intelligent Medical Engineering Lab conducts research and education that applies informatics and data science, AI, and machine learning for living bodies and medical images to imaging, diagnosis, and treatment.
Understanding the state of the human body and organs from partial observations is a fundamental challenge in medical engineering.
The lab explores machine learning models and medical system frameworks that infer biological information beyond what was observed and generate information directly tied to treatment, aiming at applications such as surgery and radiation therapy.
Undergraduates join the lab as members from their third year.
Key terms
- Machine learning
- Technology that lets computers learn rules and patterns from data
- Diffusion model
- One type of image-generation AI mechanism that gradually creates an image from noise
- Cone-beam CT
- CT that captures a 3D image at once using a cone-shaped X-ray beam. Used for patient positioning in radiation therapy, among other purposes
- Causal inference
- A method for determining from data whether a certain factor is the cause of an outcome
NewsLab news
View More ↗ResearchResearch themes
By deepening informatics methods, the lab aims to lead the automation and quality improvement of treatment processes, working toward sustainable medicine that places little burden on both doctors and patients.
*Illustrative imageEstimating the inside of the body from partial observations
To address the constraints and limits of images acquired during treatment, the lab explores deep imaging techniques that understand living bodies more deeply from partial observations.
*Illustrative imageFaster, smaller imaging with image-generation models
The lab develops image generation techniques such as diffusion models. It has confirmed that high-quality 3D images can be generated from data acquired over less than half the angular range required by conventional cone-beam CT.
*Illustrative imageAI guidance for endoscopic surgery
The lab aims to develop surgical-support AI for endoscopic and robot-assisted surgery, such as lung cancer resection and hepatectomy, that overlays the position of tumors inside organs and resection guidance onto camera images.
*Illustrative imageSystematizing diagnosis and treatment processes
Using a database of more than 600 diagnosis and treatment planning cases, the lab builds causal inference models and interpretable machine learning models to visualize how experts reason, aiming to systematize the diagnosis and treatment process.
MembersFaculty
- ProfessorMegumi Nakao
- Assistant ProfessorSho Mitarai
- Program-Specific Assistant ProfessorAkira Fujita
VoicesStudent voices
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Report an errorAbout the images
For images provided to us or used with permission, the source is listed on each line. Wikimedia Commons images are illustrative and were not taken at the lab (they are cropped). Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Cone Beam CT” Ptrump16 / CC BY-SA 4.0 (cropped)
- Estimating the inside of the body from partial observations: “Cone Beam CT principle” Aron Saar / CC BY-SA 3.0 (cropped)
- Faster, smaller imaging with image-generation models: “Current treatment principe schematic (Conventional CT - Cone-Beam CT)” WIKIMRDIA / CC BY-SA 4.0 (cropped)
- AI guidance for endoscopic surgery: “Laproscopic Surgery Robot” Nimur at English Wikipedia / CC BY-SA 3.0 (cropped)
- Systematizing diagnosis and treatment processes: “Supervised and unsupervised machine learning” Langs, G., Röhrich, S., Hofmanninger, J. et al. / CC BY 4.0 (cropped)
- Related lab (Big Data Medical Science Lab): “Eqiadwmf 9060” RobH / CC BY-SA 3.0 (cropped)
- Related lab (Cancer Occupational Therapy Lab): “Cancer Survivors Garden Pavilion” RUmtz15470 / CC BY-SA 4.0 (cropped)
- Related lab (Medical Physics Lab): “Flowchart of the standard radiomics model” Chaddad, A.; Kucharczyk, M.J.; Cheddad, A.; Clarke, S.E.; Hassan, L.; Ding, S.; Rathore, S.; Zhang, M.; Katib, Y.; Bahoric, B.; Abikhzer, G.; Probst, S.; Niazi, T. / CC BY 4.0 (cropped)
- Related lab (Neurorehabilitation Lab): “Neurorehabilitation Class” ニューロリハビリテーション研究室 / 研究室からの
提供 (cropped)
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