Accurate Localization of General Tubular Structures in Medical Images

医学图像中一般管状结构的精确定位

• 批准号: 7545744
• 负责人: Jack Noble
• 金额: $ 4.06万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7545744
• 关键词: Address; Algorithms; Anatomy; Appendix; Carotid Arteries; Cochlear Implants; Cochlear implant procedure; Ear; Electrodes; Ensure; Facial nerve structure; Funding; Grant; Head Cancer; Hospitals; Image; Invasive; Localized; Manuals; Medical; Medical Imaging; Methods; Modality; Nerve; Noise; Numbers; Optic Chiasm; Optic Nerve; Optic tract structure; Parkinson Disease; Patients; Performance; Physicians; Placement; Process; Radiation therapy; Research Personnel; Review Literature; Safety; Sigmoid colon; Sinus; Structure; Techniques; Technology; Testing; Time; Training; Tubular formation; United States National Institutes of Health; Variant; Work; cancer therapy; deafness; deep brain stimulator; design; experience; image processing; software development; success

DESCRIPTION (provided by applicant): Many medical applications require or would benefit from automatic localization of tubular structures in medical images, e.g. nerves, vessels, etc. Recently, a particular algorithm was developed which has performed robustly in localizing two nerves in the ear, the facial nerve and chorda tympani. The structure of this algorithm is open to additional designs which could address some severe shortcomings in many current localization methods. The specific aims of this project are thus to:(1) Further validate the performance of this algorithm on the facial nerve and chorda. The method has been tested on 14 CT's, taken from the same scanner, with very positive results in all cases. The method will be further validated by testing the accuracy of the algorithm on 60 CT's from four hospitals which will be made available from a current NIH funded project. (2) Develop software which automatically trains the algorithm. The algorithm must be trained once for each type of structure it is to localize. Currently, the training process is manual, very time consuming, and requires advanced image processing expertise. This aim will be accomplished through extensive mathematical and image processing literature review, comprehensive error analysis of current methods in the field, and creative experimentation. Success will be gauged by acceptability of localization results and accuracy of localizations compared to those achieved through manual tuning. (3) Extend the technology to new applications. Localization results for the sigmoid sinus, carotid artery, optic nerves, optic chiasm, and the optic tracts will be examined. MR's and CT's will be acquired from 3 NIH funded projects. Interpretation of results will be assisted by experienced physicians in the field for which each structure is relevant. Anatomical structures included in this study are relevant to three applications. Researchers validating a new minimally invasive technique for placing cochlear implants have found that the new technique is much easier and safer when four structures in this study are localized and rendered in 3D. Physicians placing deep brain stimulators have found that localization of the optic tracts would enhance electrode placement. Physicians treating head cancers with radiotherapy require accurate localization of the optic nerves, tracts, and chiasm to ensure safety from harmful effects. PULIC HEALTH RELEVANCE: The results of this project have the potential to provide physicians with anatomical information that will make cochlear implantation safer and a more available treatment for deafness, increase safety and efficiency in the placement of deep brain stimulators for treatment of Parkinson's, increase safety in the treatment of cancer by guided radiotherapy, and benefit many other applications not addressed in this study.

描述（由申请人提供）：许多医学应用需要或将从医学图像中管状结构的自动定位中受益，例如，最近，开发了一种特殊的算法，该算法在定位耳朵中的两条神经（面神经和鼓索）方面表现出色。该算法的结构对其他设计开放，可以解决许多当前定位方法中的一些严重缺陷。因此，本项目的具体目标是：（1）进一步验证该算法在面神经和腱索上的性能。该方法已在同一台扫描仪上的 14 个 CT 上进行了测试，在所有情况下都取得了非常积极的结果。该方法将通过在四家医院的 60 台 CT 上测试算法的准确性来进一步验证，这些数据将由当前 NIH 资助的项目提供。 (2)开发自动训练算法的软件。该算法必须针对要定位的每种结构类型进行一次训练。目前，培训过程是手动的，非常耗时，并且需要先进的图像处理专业知识。这一目标将通过广泛的数学和图像处理文献综述、对该领域当前方法的全面误差分析以及创造性实验来实现。成功与否将通过定位结果的可接受性以及定位的准确性（与通过手动调整实现的结果相比）来衡量。 (3)将技术扩展到新的应用领域。将检查乙状窦、颈动脉、视神经、视交叉和视束的定位结果。 MR 和 CT 将从 3 个 NIH 资助的项目中获得。结果的解释将由每个结构相关领域经验丰富的医生协助。本研究中包含的解剖结构与三个应用相关。研究人员验证了一种用于放置人工耳蜗的新型微创技术，他们发现，当本研究中的四个结构被定位并以 3D 形式呈现时，新技术变得更加容易和安全。放置深部脑刺激器的医生发现，视束的定位可以增强电极的放置。使用放射疗法治疗头部癌症的医生需要准确定位视神经、视束和视交叉，以确保安全免受有害影响。 公共健康相关性：该项目的结果有可能为医生提供解剖学信息，使人工耳蜗植入更安全，耳聋治疗更容易，提高放置深部脑刺激器治疗帕金森病的安全性和效率，提高安全性通过引导放射治疗治疗癌症，并使本研究中未涉及的许多其他应用受益。