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任洪亮:Surgical robotics and scene understanding towards augmented minimally invasive procedures
发布时间:2021-10-26 | 浏览次数:| 来源:

任洪亮:Surgical robotics and scene understanding towards augmented minimally invasive procedures

报告时间: 20211028日(星期四)下午300-5:00

报告地点:腾讯会议(会议号:651185759

人:任洪亮

工作单位:National University of Singapore

举办单位:合肥工业大学管理学院

报告简介:

Surgical robotics and scene understanding towards augmented minimally invasive procedures

Minimally Invasive Surgeries emerging in modern medical treatment have brought new opportunities and challenges for procedure-specific surgical motion generation and the associated motion understanding, which are the foundation of intelligent robotic manipulation and guiding interventions. Image-guided robotic surgery is expected to increase the precision, flexibility, and repeatability of surgical procedures but poses challenges for medical training. This talk will highlight some recent developments in dexterous robotic motion generation with motion understanding towards image-guided minimally invasive procedures. The procedure-specific telerobotic surgical systems can assist surgeons in performing dexterous manipulations using the master-slave console bilateral motion generation & mapping mechanism with variable stiffness. Meanwhile, surgical motion understanding aims to learn from the multi-domain surgical perceptions and describe the semantic relationship between instruments and surgical region of interest. Automatically understanding the instrument motions in robotic surgery is crucial to enhance surgical outcomes, enable surgical camera automation, and facilitate surgical training. To that end, we generate the task-aware saliency maps and scanpath of the instruments beyond tracking and segmentation, similar to the surgeons visual perception, to get the priority focus on selected surgical instruments. Furthermore, generating a surgical report in robot-assisted surgery, together with surgical scene understanding, can play a significant role in document entry tasks, surgical training, and post-operative analysis.

报告人简介:

Hongliang Ren received his Ph.D. in Electronic Engineering (Specialized in Biomedical Engineering) from The Chinese University of Hong Kong (CUHK) in 2008. He serves as an Associate Editor for IEEE Transactions on Automation Science & Engineering (T-ASE) and Medical & Biological Engineering & Computing (MBEC). He has navigated his academic journey through the Chinese University of Hong Kong, Johns Hopkins University, Children’s Hospital Boston, Harvard Medical School, Children’s National Medical Center, United States, and the National University of Singapore. He is currently Associate Professor, Department of Electronic Engineering at the Chinese University of Hong Kong, and Adjunct Associate Professor, Department of Biomedical Engineering at the National University of Singapore. His areas of interest include biorobotics, intelligent control, medical mechatronics, soft continuum robots, soft sensors, and multisensory learning in medical robotics. He is the recipient of NUS Young Investigator Award and Engineering Young Researcher Award, IAMBE Early Career Award 2018, Interstellar Early Career Investigator Award 2018, and ICBHI Young Investigator Award 2019.


 
 

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