Decision Support System for Temporal Lobe Epilepsy

颞叶癫痫决策支持系统

• 批准号: 8526458
• 负责人: HAMID SOLTANIAN-ZADEH
• 金额: $ 40.45万
• 依托单位: HENRY FORD HEALTH SYSTEM
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8526458
• 关键词: Amygdaloid structure; Archives; Area; Biological; Brain; Brain imaging; Characteristics; Clinical; Clinical Data; Computers; Data; Data Analyses; Databases; Decision Support Systems; Diagnosis; Diagnostic; Electrocorticogram; Electroencephalography; Epilepsy; Evaluation; Goals; Gold; Health Care Costs; Hippocampus (Brain); Human; Image; Image Analysis; Imagery; Individual; Magnetic Resonance Imaging; Manuals; Medial; Medical Imaging; Methods; Modality; Online Systems; Operative Surgical Procedures; Outcome; Patients; Pattern Recognition; Phase; Physicians; Postoperative Period; Preparation; Probability; Process; Prospective Studies; Recording of previous events; Research; Research Personnel; Retrospective Studies; Risk; Risk Factors; Role; Science; Sensitivity and Specificity; Shapes; Structure; Surface; System; Technology; Temporal Lobe; Temporal Lobe Epilepsy; Testing; Texture; Thalamic structure; Training; Validation; base; brain volume; clinical Diagnosis; clinical application; clinical decision-making; clinical practice; computerized; cost; data integration; data management; data mining; entorhinal cortex; evaluation/testing; imaging Segmentation; improved; knowledge base; multidisciplinary; novel; novel strategies; outcome forecast; predictive modeling; prospective; putamen; single photon emission computed tomography; treatment effect; treatment planning; user-friendly; web based interface

DESCRIPTION (provided by applicant): With the ever-increasing role of medical images in diagnosis, treatment planning, and evaluation of treatment effects, extraction of quantitative information from these images and efficient use of the results have become a necessity. In recent years, we have developed novel three-dimensional (3D) knowledge- based methods to segment brain structures from magnetic resonance images (MRI) automatically. These methods need to be optimized, fine-tuned, and compared to other methods for the segmentation of specific brain structures that may be involved in medial temporal lobe epilepsy (mTLE). Feature extraction methods also need to be developed and optimized to characterize (i.e., determine local and global multi- parametric intensity distribution, texture, shape, surface area, surface curvature, and volume of) the brain structures. Multi-modality analysis using multi-parametric MRI and SPECT needs to be developed for improved sensitivity and specificity. We have also developed our preliminary version of a content-based human brain image database system to hold the image analysis results with other clinical information (e.g., textual data) in a manner that can be searched, retrieved, and queried conveniently from any computer station. This system needs integrated methods for data preparation, missing value treatment, interactive rule-extraction, visualization, and user-inference to serve as a decision support system in clinical practice. A user-friendly, web-based interface will be critical for the ultimate use of the system by researchers and clinicians. Last but not least, the database needs to be populated with data from a large number of patients so that it can be confidently used for hypothesis testing and clinical applications. The goal of this project is to develop novel approaches for the above needs. Image analysis and feature extraction methods will segment and characterize hippocampus, amygdala, entorhinal cortex, thalamus, putamen, and other brain structures from MRI. The methods will be tested, evaluated, and validated using clinical data of epilepsy patients. Clinical diagnosis based on EEG studies and surgery outcome will be used as "gold standards" for evaluation and validation of the image analysis methods. The proposed decision support system will be populated with multi-modality data of 350 epilepsy patients to evaluate correlation between a variety of risk factors, imaging features, clinical diagnosis (lateralization), and post- operative outcomes, and to assist physicians with improved clinical diagnosis, reduced intracranial EEG studies (reduced risk and suffering of patients as well as their healthcare cost), optimal treatment options, and prediction of outcome in prospective studies. The proposed research will be a breakthrough in the application of computerized methods for medical image quantification and object characterization, and will advance image analysis science in the direction of integrating knowledge-based image segmentation and characterization methods with pattern recognition and data mining technology in decision support systems. The proposed approaches are applicable to the identification, segmentation, and characterization of other biological structures. They are also applicable to virtually any image analysis task for which object segmentation, quantification, and characterization are used.

描述（由申请人提供）：医学图像在诊断，治疗计划和治疗效果评估中的不断增长，从这些图像中提取定量信息以及对结果的有效使用已成为必要。近年来，我们开发了新型的三维（3D）知识方法，以自动从磁共振图像（MRI）分割大脑结构。这些方法需要优化，微调，并与其他可能涉及内侧颞叶癫痫（MTLE）的特定脑结构的方法进行了比较。还需要开发和优化特征提取方法以表征（即确定局部和全局多参数强度分布，纹理，形状，表面积，表面曲率以及大脑结构的体积）。需要开发使用多模式的多模式分析，以提高灵敏度和特异性。我们还开发了基于内容的人脑图像数据库系统的初步版本，以使用其他临床信息（例如，文本数据）来保留图像分析结果，以可以从任何计算机站方便地进行搜索，检索和查询。该系统需要集成的方法来进行数据制备，缺失价值处理，交互式规则萃取，可视化和用户推导，以作为临床实践中的决策支持系统。基于网络的用户友好，基于网络的界面对于研究人员和临床医生最终使用该系统至关重要。最后但并非最不重要的一点是，数据库需要与大量患者的数据一起填充数据库，以便可以自信地用于假设测试和临床应用。该项目的目的是为上述需求开发新颖的方法。图像分析和特征提取方法将分割并表征海马，杏仁核，内嗅皮层，丘脑，put骨和其他MRI的大脑结构。该方法将通过癫痫患者的临床数据进行测试，评估和验证。基于脑电图研究和手术结果的临床诊断将用作评估和验证图像分析方法的“金标准”。拟议的决策支持系统将使用350名癫痫患者的多模式数据填充，以评估各种风险因素，成像特征，临床诊断（横向化）和手术后结果之间的相关性，以帮助医生进行临床诊断改善的临床诊断，脑内诊断和颅内诊断的降低（可减少患者的健康状况），以及他们的健康状况，以及预测的健康状况，以及对健康的影响降低。拟议的研究将是用于用于医学图像量化和对象表征的计算机化方法的突破，并将以将基于知识的图像分割和表征与模式识别和数据挖掘技术整合到决策支持系统中的图像分析科学。所提出的方法适用于其他生物结构的识别，分割和表征。它们还适用于使用对象分割，量化和表征的任何图像分析任务。