title: A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery.
abstract: The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness.
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Shu
authors.identifier: 0000-0002-4910-6071
authors.initials: S
authors.lastname: Tian
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Xu-Cheng
authors.initials: XC
authors.lastname: Yin
authors.affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing 100089, China.
authors.forename: Zhi-Bin
authors.initials: ZB
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Zhou
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Hong-Wei
authors.initials: HW
authors.lastname: Hao
mesh_terms: D000465:Algorithms; D002386:Cataract / Q000473:pathology; D019295:Computational Biology; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data; D006801:Humans; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D018918:Phacoemulsification / Q000379:methods* / Q000706:statistics & numerical data; D025321:Surgery, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D014741:Video Recording / Q000379:methods; D020535:Video-Assisted Surgery / Q000379:methods* / Q000706:statistics & numerical data