Lung Navigation System for Localizing and Resecting Nodules

用于定位和切除结节的肺部导航系统

• 批准号: 10198924
• 负责人: RAPHAEL BUENO
• 金额: $ 36.78万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198924
• 关键词: 3-Dimensional; Address; Affect; Algorithms; Augmented Reality; Classification; Computer software; Development; Devices; Diagnosis; Diagnostic; Early treatment; Electromagnetics; Excision; Family suidae; Goals; Healthcare Systems; Legal patent; Lobectomy; Lung; Lung nodule; Machine Learning; Malignant - descriptor; Malignant neoplasm of lung; Measures; Methods; Modeling; Navigation System; Network-based; Nodule; Operative Surgical Procedures; Outcome; Patients; Performance; Phase I/II Clinical Trial; Positioning Attribute; Recurrence; Research; Structure of parenchyma of lung; Surface; Surgeon; Surgical Staplers; Surgical Staples; Surgical margins; Survival Rate; System; Technology; Thoracoscopes; Thoracoscopy; Time; Tissues; Visualization; X-Ray Computed Tomography; accurate diagnosis; arm; base; cancer diagnosis; convolutional neural network; cost estimate; deep learning algorithm; design; innovation; low dose computed tomography; lung cancer screening; machine learning algorithm; minimally invasive; novel; open source; particle; porcine model; preservation; pulmonary function; screening; sensor; tumor

Project Abstract Although Wedge Resection Surgery results in better lung function, tumor recurrence rate is almost double that of lobectomy, with significantly poorer 5-year survival rates. This may be attributed to the difficulty in accurately localizing and resecting the nodules in a deflated lung. Currently, there is no effective method of accurately localizing the nodules and guiding the surgical stapling device to the optimal resection margin. The long-term goal of this research is to investigate algorithms and technologies to manage lung nodules from diagnosis to surgical resection. The objective of this proposal is to design and develop a lung navigation (LungNav) system to localize and excise, with sufficient margin, small and early malignant lung nodules. The experimental methods will be to design and develop the LungNav system integrated with an active nodule tracker called J-bar, machine learning algorithms for determining the optimal resection margin, and tracked surgical stapling device for accurately excising the nodule. Tumor deformation algorithms and augmented reality displays will be developed to visualize the nodule on thoracosopy videos and guide the surgical stapler in real-time to the optimal margin. The hypothesis is that by anchoring the J-bar close to the nodule, the nodule position can be accurately tracked in real-time despite significant tissue deformation when the lung is collapsed and manipulated during surgery. To achieve the goals of this project, we will pursue the following specific aims: 1) Design and develop the nodule tracker (J-bar) and deformation algorithms to estimate the real-time position of the nodule. 2) Investigate a machine-learning approach based on convolutional neural networks (CNN) to determine the optimal resection margin. 3) Design and develop a software navigation module, called LungNav, for visualizing the tumor and navigating the surgical stapler to the optimal resection margin. 4) Validate the design and performance of the LungNav system using ex-vivo lung tissue and live porcine models. The proposed research is significant since it addresses an important problem, which potentially affects several thousand patients each year, of accurately localizing and resecting lung nodules while preserving healthy lung function. The research is innovative since it builds on state-of-the-art machine learning algorithms, navigation systems and augmented reality methods to accurately diagnose and localize the nodule in presence of significant tissue deformation. The expected outcome of the project is the development of CNN-based machine learning algorithms for lung nodule classification and a LungNav system with tumor deformation algorithms and augmented reality methods to localize and guide complete surgical resection of lung nodules.

项目摘要 虽然楔形切除手术可以带来更好的肺功能，但肿瘤复发率几乎 是肺叶切除术的两倍，但 5 年生存率明显较低。这可能是因为困难 准确定位并切除瘪肺中的结节。目前尚无有效的方法 准确定位结节并引导手术吻合装置达到最佳切除边缘。这 这项研究的长期目标是研究管理肺结节的算法和技术 诊断到手术切除。该提案的目标是设计和开发肺部导航 (LungNav) 系统用于定位和切除具有足够边缘的小型早期恶性肺结节。这 实验方法将是设计和开发与活动结节集成的 LungNav 系统 称为 J-bar 的跟踪器，用于确定最佳切除边缘的机器学习算法，并跟踪 用于精确切除结节的手术缝合装置。肿瘤变形算法和增强 将开发现实显示器，以在胸腔镜检查视频中可视化结节并指导手术缝合器 实时达到最佳裕度。假设是，通过将 J 杆锚定在靠近结节的位置，结节 尽管肺部塌陷时组织发生显着变形，但仍可以实时准确跟踪位置 并在手术期间进行操纵。为了实现该项目的目标，我们将追求以下具体目标： 1）设计和开发结节跟踪器（J-bar）和变形算法来估计实时位置 结节的。 2）研究基于卷积神经网络（CNN）的机器学习方法 确定最佳切除边缘。 3）设计并开发一个软件导航模块，称为LungNav， 用于可视化肿瘤并将手术吻合器导航至最佳切除边缘。 4) 验证 使用离体肺组织和活体猪模型设计和性能 LungNav 系统。这 拟议的研究意义重大，因为它解决了一个重要问题，可能会影响几个方面 每年为数千名患者准确定位和切除肺结节，同时保留健康的肺部 功能。这项研究具有创新性，因为它建立在最先进的机器学习算法、导航 系统和增强现实方法可以在存在的情况下准确诊断和定位结节 显着的组织变形。该项目的预期成果是开发基于 CNN 的机器 肺结节分类的学习算法和具有肿瘤变形算法的 LungNav 系统 增强现实方法定位和指导肺结节的完整手术切除。

项目成果

Image-guided video-assisted thoracoscopic resection (iVATS): Translation to clinical practice-real-world experience.

图像引导视频辅助胸腔镜切除术（iVATS）：转化为临床实践-现实世界经验。

• 发表时间: 2020-06
• 影响因子: 2.5
• 作者: Gill, Ritu R;Barlow, Julianne;Jaklitsch, Michael T;Schmidlin, Eric J;Hartigan, Phillip M;Bueno, Raphael
• 通讯作者: Bueno, Raphael

Clinical and molecular validation of BAP1, MTAP, P53, and Merlin immunohistochemistry in diagnosis of pleural mesothelioma.

BAP1、MTAP、P53 和 Merlin 免疫组织化学诊断胸膜间皮瘤的临床和分子验证。

• 发表时间: 2022-10
• 作者: Chapel, David B;Hornick, Jason L;Barlow, Julianne;Bueno, Raphael;Sholl, Lynette M
• 通讯作者: Sholl, Lynette M

EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction

EndoGaussian：用于动态内窥镜场景重建的实时高斯喷射

• DOI: 10.48550/arxiv.2403.06683
• 发表时间: 2024-01-23
• 期刊: ArXiv
• 作者: Yifan Liu;Chenxin Li;Chen Yang;Yixuan Yuan
• 通讯作者: Yixuan Yuan

Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.

使用 3 维内窥镜和表面重建在前颅底进行手术导航。

• 发表时间: 2024-04-01
• 作者: Bartholomew, Ryan A;Zhou, Haoyin;Boreel, Maud;Suresh, Krish;Gupta, Saksham;Mitchell, Margaret B;Hong, Christopher;Lee, Stella E;Smith, Timothy R;Guenette, Jeffrey P;Corrales, C Eduardo;Jagadeesan, Jayender
• 通讯作者: Jagadeesan, Jayender