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

• 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
• 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

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.

项目总结/文摘