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: 7488310
• 负责人: ANKE ANKE_MEYER-BAESE MEYER-BAESE
• 金额: $ 13.86万
• 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 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7488310
• 关键词: Algorithms; Biological; Biological Neural Networks; Biopsy; Breast; Caliber; Cereals; Chicago; Classification; Computer Assisted; Computer software; Computer-Assisted Diagnosis; Computers; Contrast Media; Data; Data Analyses; Databases; Decision Making; Diagnosis; Diagnostic; Diagnostic radiologic examination; Disadvantaged; Engineering; Estimation Techniques; Evaluation; Gases; Goals; Healthcare; Image; Imagery; K-Series Research Career Programs; Laboratories; Lead; Lesion; Magnetic Resonance Imaging; Malignant - descriptor; Mammary Gland Parenchyma; Mammography; Maps; Mentors; Meta-Analysis; Methodology; Methods; Metric; Outcome; Patients; Pattern; Pattern Recognition; Performance; Perfusion; Politics; Process; Property; Protocols documentation; Quantitative Evaluations; Radiology Specialty; Research; Research Personnel; Research Technics; Sensory Process; Series; Signal Transduction; Solutions; Specimen; Structure; System; Techniques; Testing; Time; Tissues; Training; Universities; anticancer research; base; biological systems; breast cancer diagnosis; breast lesion; career; design; diagnostic accuracy; improved; independent component analysis; novel; oncology; programs; radiologist; relating to nervous system; skills; tool; uptake

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）：用于乳腺 MRI 的 CAD 中使用的标准技术基于监督人工神经网络，并且已显示出不令人满意的判别结果和有限的应用能力。这些技术的主要缺点是：(1) 需要固定的 MR 成像协议，(2) 诊断直径只有几毫米的小乳腺肿块很困难，(3) 无法捕获病变结构，(4) 由于病变数据池不均匀而导致训练限制。为了克服上述问题，本研究计划的主题是采用生物神经网络，严格关注观察到的完整 MRI 信号时间序列，并实现动态对比增强乳腺 MRI 时间序列的自组织数据驱动分割。信号幅度和动态的细粒度差异，例如不确定乳腺病变患者的局灶性增强。本项目的目标是在跨学科框架内提高乳腺 MRI 的诊断质量。具体来说，该项目的目标是：(1) 开发、评估和测试新型神经网络技术，用于动态对比增强乳腺 MRI 数据的功能和结构分割、可视化和分类，从而 (2) 通过改进对传统 X 射线乳房 X 线摄影检测到的可疑病变的进一步评估，为乳腺癌诊断做出重大贡献。 PI 是一位具有模式识别背景的电气和计算机工程师，他一直在开发源自最新生物发现的新分类方法，旨在模仿生物系统中的决策和感知处理。该指导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，并利用这些技能将这些新的理论工具扩展并有效地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生物医学应用领域的有效研究人员，特别是计算机辅助诊断。拟议研究的结果预计将对医疗保健政治产生重大影响，有助于通过非侵入性成像诊断不确定的乳腺病变。