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）：许多医疗申请要求或将受益于医疗图像中管状结构的自动定位，例如神经，血管等。最近，开发了一种特殊的算法，该算法在将两只神经局部定位在耳朵，面神经和鸡冠中。该算法的结构对其他设计开放，这些设计可能解决了许多当前定位方法中的一些严重缺点。因此，该项目的具体目的是：（1）进一步验证该算法在面神经和鸡冠上的性能。该方法已在14个CT上进行了测试，从同一扫描仪中取出，在所有情况下都非常积极。该方法将通过测试四家医院的60 CT算法的准确性进一步验证，这些算法将从当前的NIH资助项目中获得。 （2）开发自动训练算法的软件。必须对算法进行一次训练，每种类型的结构要定位。目前，培训过程是手动的，非常耗时，需要高级图像处理专业知识。这个目标将通过广泛的数学和图像处理文献综述，对现场当前方法的全面错误分析以及创造性的实验来实现。与通过手动调整获得的本地化结果的可接受性和本地化的准确性相比，将通过可接受性能的可接受性来衡量成功。 （3）将技术扩展到新应用程序。将检查Sigmoid窦，颈动脉，视神经，视神经chiasm和视线的定位结果。 MR和CT将从3个由NIH资助的项目中获取。结果的解释将由每个结构相关的现场经验丰富的医生提供帮助。本研究中包括的解剖结构与三个应用有关。研究人员验证了一种用于放置人工耳蜗的新型侵入性技术的研究人员发现，当本研究中的四个结构被定位并在3D中渲染时，新技术更容易，更安全。放置深脑刺激剂的医师发现，视线的定位将增强电极的位置。治疗放射治疗的头部癌症的医生需要对视神经，区域和CHIASM进行准确的定位，以确保从有害影响中安全。 PULIC健康相关性：该项目的结果有可能为医生提供解剖信息，从而使人工耳蜗植入更安全，并提供更可用的聋哑治疗方法，提高对帕金森氏症治疗的深脑刺激剂的安全性和效率，从而通过指导性的放射治疗来增加癌症治疗的安全性，并使许多其他应用程序受益于该研究。