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- 出生日期：1983-09-02
- 电子邮箱：zhaowang92@uestc.edu.cn
- 入职时间：2018-06-01
- 学历：博士研究生毕业
- 办公地点：清水河科研楼4号C区336房间
- 性别：男
- 学位：哲学博士学位
- 职称：教授
- 博士生导师
- 曾获荣誉：国家青年特聘专家，荣获四川省“天府峨眉计划”、成都市“四派人才”、“蓉漂计划”称号
- 学科：电子科学与技术
物理电子学
Computer-Aided Intraoperative Toric Intraocular Lens Positioning and Alignment During Cataract Surgery
- 点击次数：
- 所属单位：[1]Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 610054, Sichuan, Peoples R China;[2]Shanxi Intelligence Inst Big Data Technol & Innov, Taiyuan 030000, Shanxi, Peoples R China;[3]Taiyuan Univ, Dept Comp Engn, Taiyuan 030000, Shanxi, Peoples R China;[4]Shanxi Eye Hosp, Taiyuan 030002, Shanxi, Peoples R China
- 发表刊物：IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
- 关键字：Surgery; Cataracts; Iris; Pupils; Manuals; Videos; Training; Cataract Surgery; Convolutional Neural Networks; Tracking
- 摘要：Cataract causes more than half of all blindness worldwide. The most effective treatment is surgery, where cataract is often replaced by intraocular lens (IOL). Beyond saving vision, toric IOL implantation is becoming increasingly popular to correct corneal astigmatism. It is important to precisely position and align the axis of IOL during surgery to achieve optimal post-operative astigmatism correction. Comparing with conventional manual marking, automated markerless IOL alignment can be faster, more accurate and non-invasive. Here we propose a framework for computer-assisted intraoperative IOL positioning and alignment based on detection and tracking. Firstly, the iris boundary was segmented and the eye center was determined. A statistical sampling method was developed to segment iris and generate training labels, and both conventional algorithms and deep convolutional neural network (CNN) methods were evaluated. Then, regions of interests (ROIs) containing high density of scleral capillaries were used for tracking eye rotations. Both correlation filter and CNN methods were evaluated for tracking. Cumulative errors during long-term tracking were corrected using a reference image. Validation studies against manual labeling using 7 clinical cataract surgical videos demonstrated that the proposed algorithm achieved an average position error around 0.2 mm, an axis alignment error of < 1 degrees, and a frame rate of > 25 FPS, and can be potentially used intraoperatively for markerless IOL positioning and alignment during cataract surgery.
- 文献类型：Article
- 卷号：25
- 期号：10
- 页面范围：3921-3932
- ISSN号：2168-2194
- 是否译文：否