千葉大学国際医工学セミナー

International Seminar Series on Biomedical Engineering

第10回
日時：2011年7月22日（金）17:00 - 18:30
場所：千葉大学ＣＦＭＥ　Ｂ号棟１階会議室　（西千葉キャンパス）

＜演題１＞

タイトル

Clinical-driven Digital Technology in Orthopedic Implant, Computer-assisted Orthopedic Surgery and RoboNurse

演者

Dr. Dong-Yun Gu
(Senior Engineer and Director of Executive Office, Engineering Research Center of Digital Medicine, Ministry of Education of P. R. China)

概要

Clinical requirements prompt the integration of digital techniques in orthopedic clinical practice. This presentation will review our recent work in the development of the individualized prosthesis with CAD/CAM and rapid prototype technique, the computer-assisted orthopedic surgery and the nurse lifting and transfer injury simulation for RoboNurse development.

＜演題２＞

タイトル

Digital Medicine & Rehabilitation Engineering

演者

Dr. Le Xie
(Professor of Engineering Research Center of Digital Medicine, Ministry of Education of P. R. China, Deputy Director of Rehabilitation Engineering Institute, Shanghai Jiao Tong University)

概要

The application of the digital manufacturing and Virtual reality technology in digital medicine and rehabilitation engineering are introduced. The research of virtual surgery with haptic feedback and the minimally invasive surgery assist robot are discussed.

＜演題３＞

タイトル

Early Diagnosis and Treatment for Aseptic Loosening and Motion Block of Artificial Joints

演者

Dr. Jin-Wu Wang
(Associate Professor of Engineering Research Center of Digital Medicine, Ministry of Education of P. R. China)

概要

Develop and verify a three-dimensional, real-time, quantitative, simple and universal way to examine aseptic loosening and movement blocking in the joint motion state after artificial joint replacement.

＜演題４＞

タイトル

Computer-aided Detection of Ultrasound Images for Breast Tumors

演者

Dr. Su Zhang
(Associate Professor of Engineering Research Center of Digital Medicine, Ministry of Education of P. R. China)

概要

Various computerized features extracted from breast ultrasound images are useful in assessing the malignancy of breast tumors. We use the decision tree ensemble trained by the cost-sensitive boosting algorithm to approximate the target function for malignancy assessment and to reflect this relationship qualitatively. In the experiments, 31 image features are extracted to quantify the sonographic characteristics of breast tumors. The results can provide visual and qualitative references on the computerized image features for physicians, and can be useful for enhancing the interpretability of computer-aided detection systems for breast ultrasound.