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: 0653097
• 负责人: JungHwan Oh
• 金额: --
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-08 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0653097&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.

视频技术的进步正在融入当今的医疗实践中。例如，各种类型的内窥镜用于结肠镜检查、上消化道内窥镜检查、肠镜检查、支气管镜检查和膀胱镜检查。此外，数量迅速增加的以前开放的外科手术现在正在转变为内窥镜手术，包括切除胆囊、取出供体肾脏、切除结肠和胰腺肿瘤、矫正裂孔疝气、冠状动脉旁路移植术和微创神经外科手术。尽管在医学图像分析方面有大量的知识，但内窥镜检查视频并没有被系统地捕获用于实时或手术后的审查和分析。它们偶尔会被录制到磁带上(即录像带)。还没有开发硬件和软件工具来捕获、分析和提供对此类视频上的医疗、科学或教育内容的用户友好和高效访问。该项目的目的是开发一个内窥镜多媒体信息系统(EMIS)来捕获高质量的内窥镜视频，分析捕获的视频，并提供对这些视频内容的有效访问。该项目将开发一种新的内窥镜视频捕获系统，专为患者隐私而设计，不会对内窥镜检查程序造成干扰，也不会限制特定的内窥镜检查供应商。信息性框架和非信息性框架自动分类的新算法。该技术不需要任何预定义的参数或阈值。用于息肉等突出病变的自动内容分析的新算法。许多隆起的病变聚集在一起。首先将开发一种新的区域分割技术，可以识别孤立的病变。为了处理聚集的病变，将开发使用区域模式图的算法，该算法捕捉相关区域的重要特征。建议的系统将直接有利于内窥镜研究、教育和培训。为研究生的研究型培训做出贡献，这些研究生将为PI和COPI机构的计算机科学和医学信息学研究生和本科生的研究型高级培训做出贡献。该项目将有助于培养新一代计算机科学家，为传统医学成像提供独特的技能补充。这项研究将为参加各种大学项目(UTA夏令营、女性科学与工程项目)和国家项目的初中生和高中生提供更多的研究机会。(4)扩大代表不足群体的参与。