Real-time Non-Rigid 3D Reconstruction and Registration for Laparoscopic-guided Minimally Invasive Liver Surgery

腹腔镜引导微创肝脏手术的实时非刚性 3D 重建和配准

基本信息

批准号：
10611559
负责人：
Haoyin Zhou
金额：
$ 24.9万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10611559
关键词：
3-Dimensional Address Algorithms Anatomy Applications Grants Area Award Basic Science Binocular Vision Blood Carbon Dioxide Clinical Clinical Research Color Computer Vision Systems Computer software Data Dependence Devices Diagnosis Diagnostic Environment Excision Family suidae Feedback Funding Goals Healthcare Hemorrhage Hepatic Hospitals Image International Knowledge Laboratories Laparoscopes Laparoscopy Learning Liver Liver neoplasms Location Machine Learning Malignant neoplasm of liver Maps Medical Medical Imaging Medicine Mentors Methods Modeling Motion Multimodal Imaging Navigation System Operating Rooms Operative Surgical Procedures Patients Phase Pneumoperitoneum Postdoctoral Fellow Procedures Psyche structure Recovery Research Research Personnel Research Support Respiration Robotics Solid Stress Structure Supervision Surface Surgeon Surgical Instruments System Technology Texture Time Tissues Titan Training Training and Education Translational Research Trauma patient Ultrasonography Uncertainty United States National Institutes of Health Woman Work Writing X-Ray Computed Tomography base cancer diagnosis career career development catalyst deep learning design detection method disorder prevention experience frontier haptic feedback human disease image guided image guided therapy image processing image registration imaging facilities improved in vivo machine learning algorithm machine learning method medical schools minimally invasive novel postoperative recovery professor programs prototype radiologist reconstruction research and development research clinical testing symposium technology development three-dimensional visualization tissue reconstruction tool tumor uptake virtual patient

项目摘要

Project Summary/Abstract Liver deformation leads to difficulties in tumor localization during minimally invasive liver surgery (MILS). The goal of this proposal is to develop an efficient surgical navigation tool for MILS by compensating for liver deformation and mapping preoperative data to the patient’s anatomy. Specifically, we will develop a non-rigid simultaneously localization and mapping (SLAM) approach to estimate the deformation of liver surface from stereo laparoscopy videos. We will develop machine-learning methods to detect landmarks and perform non- rigid registration. The algorithms will be implemented on a GPU to achieve real-time. Preliminary data has demonstrated the feasibility. During the R00 phase, we will mainly address the clinical needs and develop novel ways to provide intraoperative guidance. This project will greatly improve the tumor resection accuracy in MILS. The candidate for this award Dr. Haoyin Zhou is a postdoc at Surgical Planning Laboratory (SPL), Brigham and Women’s Hospital (BWH) and Harvard Medical School (HMS). Dr. Zhou has extensive experience and expertise in computer vision, machine learning and their applications in medicine. BWH is an international leader in basic, clinical and translational research on human diseases, and has established multiple research programs to promote the work and professional career development of young investigators. National Center for Image Guided Therapy, and Advanced Multi-modality Image Guided Operating (AMIGO) suite will greatly support this research. Dr. Zhou’s long-term research goal is to develop and apply advanced computer vision and machine learning technologies to improve understanding, diagnosis, treatment, and prevention of diseases for better health care. His long-term career goal is to become an independent investigator working at the frontier of medical image processing and image-guided therapy. To achieve these goals, Dr. Zhou plans to receive more education and training in the following four areas: (1) Critical training in conducting translational research in the hospital environment with surgeons and radiologists, (2) knowledge in the development of technologies for surgical guidance, (3) training in machine learning and its applications in medicine, and (4) training on writing grant applications independently and seeking funding. Dr. Zhou will participate in formal courses selected from Harvard, Harvard Catalyst, MIT CSAIL and Stanford Courses. He will attend weekly seminars at BWH, HMS and MIT. He will also attend one or two academic conferences per year to discuss his work and meet with experts in the field. A strong mentoring team, including one primary mentor, three co-mentors, and two collaborators, has been organized for the K99 phase of this award, which will provide solid support on both research and career development to Dr. Zhou based on their well-established expertise in diverse research fields. Prof. William M. Wells III (primary mentor) is a professor in medical image processing. Prof. Jayender Jagadeesan (co-mentor) is an assistant professor in surgical robotics and surgical navigation. Drs. Ali Tavakkoli and Jiping Wang (co- mentors) are experienced surgeons. All mentors and collaborators are from BWH, HMS.

项目摘要/摘要肝脏变形导致在微创肝手术（MILS）期间肿瘤定位的差异该提案的目标是开发MILS MILS MILS的效率外科导航工具术前数据的变形和映射到患者的解剖结构中。同时定位和映射方法（大满贯）方法以估计肝表面的变形立体声视频。严格的注册在R00阶段展示了盛宴，我们将主要满足临床需求提供术中指导的方式。奖学博士的候选人在周的Haoyin Zhou是外科计划实验室（SPL），Brigham和妇女医院（BWH）和哈佛医学院（HMS）。在计算机视觉中，机器学习和医学中的应用是基本的国际领导者人类疾病的临床和翻译研究，并建立了多个研究计划促进年轻调查员的工作和职业职业发展。治疗和先进的多模式图像图像指导操作（Amigo）套件将极大地支持这项研究。周博士的长期研究目标是开发和应用高级计算机视觉和机器学习改善理解，诊断，治疗和生产的技术。他的长期职业目标是成为在医学形象边界工作的独立研究员 D. Zhou计划接受更多的教育和在以下四个领域进行培训：（1）在医院进行转化研究的重要培训与外科医生和放射学家有关的环境，（2）手术技术开发方面的知识指导，（3）机器学习的培训，是医学的应用，（4）写作授予培训独立申请并寻求资金。哈佛大学，麻省理工学院和斯坦福大学的课程还将参加珀斯一年或两个学术会议，讨论Hiss工作并与该领域的专家会面。一个强大的指导团队，包括一名主要导师，三名联合官员和两个合作者为该奖项的K99阶段组织，该奖项将为研究和职业提供可靠的支持基于多元化研究领域的公认训练向周博士开发。 Wells III（主要导师）是Jayender Jagadeesan教授的教授是外科机器人和外科导航的助理教授。导师是经验丰富的外科医生。