CAD in Breast MRI based on Biological Neural Network

基于生物神经网络的乳腺MRI CAD

• 批准号: 6875352
• 负责人: ANKE ANKE_MEYER-BAESE MEYER-BAESE
• 金额: $ 13.96万
• 依托单位: FLORIDA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-21 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6875352
• 关键词: artificial intelligence; bioimaging /biomedical imaging; breast neoplasm /cancer diagnosis; breast neoplasms; computer assisted diagnosis; computer assisted medical decision making; diagnosis design /evaluation; diagnosis quality /standard; female; human data; image enhancement; magnetic resonance imaging; neoplasm /cancer classification /staging; noninvasive diagnosis; women' s health

DESCRIPTION (provided by applicant): Standard techniques used in CAD for breast MRI are based on supervised artificial neural networks and have shown unsatisfactory discriminative results and limited application capabilities. The major disadvantages associated with these techniques are: (1) requirement of a fixed MR imaging protocol, (2) difficulties in diagnosing small breast masses with a diameter of only a few mm, (3) incapacity of capturing the lesion structure, and (4) training limitations due to an inhomogeneous lesions data pool. To overcome the above mentioned problems, the theme of this research plan becomes to employ biological neural networks which focus strictly on the observed complete MRI signal time-series, and enable a self-organized data-driven segmentation of dynamic contrast-enhanced breast MRI time-series w.r.t. fine-grained differences of signal amplitude, and dynamics, such as focal enhancement in patients with indeterminate breast lesions. The goal of the present project is to improve in an interdisciplinary framework the diagnostic quality in breast MRI. Specifically, the objectives of this proposed project are to: (1) develop, evaluate and test novel neural network techniques for functional and structural segmentation, visualization, and classification of dynamic contrast-enhanced breast MRI data, and thus, (2) substantially contribute to breast cancer diagnosis by improved further evaluation of suspicious lesions detected by conventional X-ray mammography. The PI is an electrical and computer engineer with a background in pattern recognition who has been developing new classification methods derived from the newest biological discoveries aiming to imitate decision-making, and sensory processing in biological systems. This Mentored Quantitative Research Career Development Award will permit the PI to acquire training in cancer research techniques and in computer assisted radiology, and to use these skills to extend and productively apply these new theoretical tools to biomedical applications. Accordingly, the long-term career goal of the PI is to become an effective researcher in the biomedical applications of pattern recognition, with specific emphasis in computer-aided diagnosis. The outcome of the proposed research is expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

描述（由申请人提供）：CAD中用于乳房MRI的标准技术基于监督的人工神经网络，并且显示出不令人满意的判别结果和有限的应用功能。与这些技术相关的主要缺点是：（1）固定的MR成像方案的要求，（2）诊断直径仅几毫米的小乳房肿块的困难，（3）捕获病变结构的无能，以及（4）由于均匀的病变数据池而引起的训练限制。为了克服上述问题，该研究计划的主题是采用生物神经网络，这些神经网络严格关注观察到的完整MRI信号时间序列，并启用自组织的数据驱动的动态对比度增强乳房MRI时间序列的分割W.R.T.信号振幅和动力学的细粒差异，例如不确定乳腺病变患者的焦点增强。本项目的目的是在跨学科框架中改善乳房MRI的诊断质量。具体而言，该提出的项目的目标是：（1）开发，评估和测试新型的神经网络技术，以进行功能和结构细分，可视化和分类动态对比增强的乳腺MRI数据，（2），（2）通过对常规X-Ray X-Ray X-Ray Mammosemss的可疑病情的进一步评估，通过进一步评估可疑的病态来改善乳腺癌诊断。 PI是具有模式识别背景背景的电气和计算机工程师，他一直在开发旨在模仿决策和生物系统中感觉处理的最新生物学发现的新分类方法。这项受过指导的定量研究职业发展奖将使PI能够获得癌症研究技术和计算机辅助放射学方面的培训，并使用这些技能扩展并有效地将这些新的理论工具应用于生物医学应用。因此，PI的长期职业目标是成为模式识别的生物医学应用中的有效研究人员，并在计算机辅助诊断中特别强调。预计拟议研究的结果有望通过通过非侵入性成像诊断不确定的乳房病变来对医疗保健政治产生重大影响。