SEI: Collaborative Research: Endoscopic Multimedia Information System (EMIS)

SEI：合作研究：内窥镜多媒体信息系统（EMIS）

• 批准号: 0513777
• 负责人: JungHwan Oh
• 金额: $ 20.3万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0513777&HistoricalAwards=false
• 关键词: SEI; Collaborative; Research; Endoscopic; Multimedia

ABSTRACTAdvances in video technology are being incorporated into today's healthcare practice. For example, various types of endoscopes are used for colonoscopy, upper gastrointestinal endoscopy, enteroscopy, bronchoscopy, and cystoscopy. In addition, a rapidly expanding number of formerly open surgical procedures now are being converted to endoscopic procedures including resection of gallbladders, retrieval of donor kidneys, resection of tumors of colon and pancreas, correction of hiatal hernias, coronary artery bypass grafting and minimal invasive neurosurgeries. Despite a large body of knowledge in medical image analysis, endoscopy videos are not systematically captured for real-time or post-procedure reviews and analyses. They are recorded occasionally to magnetic video-tapes (i.e., VHS). No hardware and software tools have been developed to capture, analyze, and provide user-friendly and efficient access to the medical, scientific, or educational content on such videos. This project aims to develop an Endoscopic Multimedia Information System (EMIS) to capture high quality endoscopy videos, analyze the captured videos, and provide efficient access to the content of these videos.Images of endoscopy videos significantly differ from medical still images as studiedin the literature of medical image processing. The project will develop a new capturing system for endoscopy videos designed for patient's privacy and is non-disruptive to endoscopic proceduresand non-restrictive to a particular endoscope vendor. New algorithms for automatic classification of informative and non-informative frames. The technique does not need any predefined parameters or thresholds. New algorithms for automatic content analysis for protruding lesions such aspolyps. Many protruding lesions are clustered together. A new region segmentation technique that can identify isolated lesions will be developed first. To handle clusteredlesions, algorithms using a region pattern graph that captures important characteristics of relevant regions will be developed..The proposed system will directly benefit endoscopic research, education, and training. Contributions to research-based training of graduate students who will contribute to research-based advanced training of students in graduate and undergraduate programs in computer science and medical informatics at PI and CoPI's institutions. The project will contribute to training of a new generation of computer scientists with a unique skill set supplement to traditionalmedical imaging. This research will enhance research opportunities for junior high and high school students participating in various university programs (UTA Summer Science Camp, Program for Women in Science and Engineering) and national programs. (4) Broaden the participation of under-represented groups.

视频技术中的抽象吸引力正在当今的医疗保健实践中纳入。例如，各种类型的内窥镜用于结肠镜检查，上胃肠道内窥镜检查，肠镜检查，支气管镜检查和膀胱镜检查。此外，现在正在转换一些以前的开放式外科手术，转换为内窥镜检查程序，包括切除胆囊，供体肾脏的检索，结肠和胰腺肿瘤的切除，裂孔疝气的纠正，冠状动脉旁路侧面嫁接和最小入侵性神经疗法。尽管在医学图像分析方面有很多知识，但内窥镜视频并未系统地捕获用于实时或后处理的评论和分析。它们偶尔记录到磁性视频录音带（即VHS）。没有开发硬件和软件工具来捕获，分析和提供对此类视频的医学，科学或教育内容的用户友好，有效的访问。该项目旨在开发内窥镜多媒体信息系统（EMIS），以捕获高质量的内窥镜视频，分析捕获的视频并有效地访问这些视频的内容。内窥镜视频的图像与医学图像中的医学图像明显差异，以医学图像处理的文献研究。该项目将开发一种新的捕获系统，用于用于患者隐私的内窥镜视频，并且对内窥镜检查和对特定内窥镜供应商的无限制性无障碍。自动分类信息和非信息框架的新算法。该技术不需要任何预定义的参数或阈值。用于自动含量分析的新算法，用于突出病变，例如肌瘤。许多突出的病变聚集在一起。可以首先开发一种可以识别孤立病变的新区域分割技术。为了处理聚类的情况，将开发使用捕获相关区域重要特征的区域模式图的算法。拟议的系统将直接受益于内窥镜研究，教育和培训。对研究生的基于研究的培训的贡献，这些培训将为PI和COPI机构的计算机科学和医学信息学研究生和本科课程的基于研究的高级培训做出贡献。该项目将有助于培训新一代的计算机科学家，并具有独特的技能集补充剂，以补充传统的成像。这项研究将为参加各种大学课程（UTA夏季科学训练营，科学与工程学计划）和国家计划的初中和高中生提供研究机会。 （4）扩大代表性不足的群体的参与。