Real-Time Bronchoscope Localization Using Machine Learning To Improve Lung Cancer Diagnosis

项目总结/摘要 肺癌的5年生存率从早期诊断的61%下降到晚期诊断的6%。 目前，在早期阶段诊断的病例不到五分之一。胸部CT扫描频率的增加 肺癌筛查指南的变化预计会增加偶然发现的肺癌数量。 肺部病变，代表了早期肺癌诊断的机会。支气管镜检查是目前最安全的， 最小侵入性和最便宜的诊断选择，但其诊断率低，大大限制了其程序 贝内.即使先进的技术如放射状支气管内超声和电磁导航 诊断率仅为50- 60%。这主要是由于术中定位的挑战 在针展开之前的支气管镜。此外，获得这些技术是有限的，因为它们 需要昂贵的设备和独特的专业知识。依靠支气管镜内置摄像头的努力需要 没有额外的设备或专业化，但在一定程度上由于个人之间的普遍性而挣扎， 有限的数据可用性和关于气道特征的假设。 该提案的目的是通过解决传统支气管镜的局限性来提高成功率， 使用对人体解剖结构差异具有鲁棒性的数据驱动模型进行术中定位。这 工作有可能通过以下方式为公共卫生带来重大贝内：（1）增加早期肺癌检测，（2）减少 发病率和死亡率，减少侵入性程序的数量，和（3）使微创的布朗- 在没有支气管镜专家的地区更容易进行支气管镜检查。拟议的工作将通过以下方式完成： 两个具体目标。在目标1中，将生成具有视频帧的虚拟和真实的支气管镜检查视频的数据集 匹配的六个自由度的姿态（在三维的位置和方向）的支气管镜的dis- 谈话提示。这些数据将作为第一个大型数据集公开提供，以促进未来的研究， 再现性在目标2中，将利用MA的进展开发实时支气管镜定位模型， 中国学习，包括深度神经网络，在非医疗摄像头定位方面取得了成功 应用.这些模型将使用当前和过去的视频回归支气管镜远端头端的姿势 支气管镜内置摄像头的帧。将在3D模拟中评估该系统的临床实用性 打印的肺模型和离体猪肺实验。这项研究，紧密结合的临床经验， 和相关的培训计划将提供一个独特的跨学科技能，在计算机科学，医疗机器人， 和程序医学。杰出的研究和临床环境，在大学的这种培训 查佩尔山的北卡罗来纳州确保为从事尖端研究的职业做好出色的准备， 医学机器人领域的医生兼科学家

Project Summary/Abstract Lung cancer 5-year survival rates drop from 61% for early stage diagnosis to just 6% for late stage diagnosis. Currently, fewer than 1 in 5 cases are diagnosed at an early stage. The increasing frequency of chest CT scans and changes in lung cancer screening guidelines are expected to increase the number of incidentally discovered lung lesions, representing an opportunity for earlier lung cancer diagnosis. Bronchoscopy is currently the safest, least invasive, and least expensive diagnostic option, but its poor diagnostic yield greatly limits its procedural benefit. Even when advanced techniques like radial endobronchial ultrasound and electromagnetic navigation are used, the diagnostic yield is just 50-60%. This is primarily due to challenges with intraoperative localization of the bronchoscope prior to needle deployment. Additionally, access to these techniques is limited because they require expensive equipment and unique expertise. Efforts relying on the bronchoscope's built-in camera require no additional equipment or specialization, but have struggled with generalizability across individuals in part due to limited data availability and assumptions about airway features. The objective of this proposal is to improve the success rate of traditional bronchoscopes by addressing limita- tions in intraoperative localization using a data-driven model that is robust to differences in human anatomy. This work has potential for significant public health benefit by (1) increasing early lung cancer detection, (2) reducing morbidity and mortality by reducing the number of invasive procedures, and (3) making minimally invasive bron- choscopy more accessible in areas without expert bronchoscopists. The proposed work will be accomplished via two Specific Aims. In Aim 1, a dataset will be generated of virtual and real bronchoscopy videos with video-frame matched six degrees-of-freedom poses (position and orientation in three-dimensions) of the bronchoscope's dis- tal tip. This data will be made publicly available as the first large dataset of its kind to promote future research and reproducibility. In Aim 2, a real-time bronchoscope localization model will be developed using advances in ma- chine learning, including deep neural networks, that have shown success in camera localization for non-medical applications. These models will regress the pose of the bronchoscope's distal tip using current and past video frames of the bronchoscope's built-in camera. The clinical utility of the system will be evaluated in simulation, 3D printed lung phantoms, and ex-vivo porcine lung experiments. The research, tightly coupled clinical experience, and associated training plan will provide a unique interdisciplinary skill-set in computer science, medical robotics, and procedural medicine. The outstanding research and clinical environment for this training at the University of North Carolina at Chapel Hill ensures exceptional preparation for a career conducting cutting-edge research as a physician-scientist in medical robotics.