Multiresolution-fractal modeling for brain tumor detection

描述（由申请人提供）：PI的长期研究目标是开发一种功能齐全的自动化健壮的CAD工具，用于准确的儿童脑肿瘤体积分割和跟踪。注意，目前脑肿瘤体积分割的做法是在多模态MRI中手工追踪和分割疑似肿瘤区域，这是耗时、劳动密集型的，而且可能不精确。为了减少认知后遗症，当代治疗方案采用风险适应疗法，其中风险分层基于手术切除后残留肿瘤的体积和诊断时是否存在转移性疾病。因此，如果不提高对肿瘤体积和分类等因素的认识，儿童癌症治疗结果的进一步改善是不可能实现的。此外，这种自动体积计算和跟踪工具在脑肿瘤患者随访中具有辅助标记价值。反过来，这将帮助医生做出重要的病人管理决定，包括手术计划，关键放射治疗计划修改，治疗领域修改，局部控制，转移性疾病部位和治疗后反应评估。然而，这种自动化、精确的肿瘤体积分割CAD工具的开发需要解决一些挑战，如难以检测的脑肿瘤（术后残留小、增强差、多病灶、形状不规则）的检测和异常（手术导致水肿、坏死、切除腔较大）的检测和分类。该项目旨在开发、测试和评估创新技术和工具，以帮助基于特征的脑肿瘤和一些特定异常的检测、分割和分类。本课题的具体目标是：1)样条-多分辨率小波-分形特征提取；2) MR序列依赖的特征融合和肿瘤/异常大小和体积的确定，以改进检测；3)优化特征融合，改进肿瘤、组织和异常分类；4)算法测试与验证。{如果成功，我们的方法将允许以更高的准确性自动计算脑肿瘤和异常，这可以提供快速、客观、可重复和易于报告的疾病评估。通过提供一种准确、客观、一致的方法来评估和解释脑肿瘤及相关异常，本项目获得的结果将对儿科神经放射学实践产生直接影响。

DESCRIPTION (provided by applicant): The PI's long-term research goal is to develop a fully functional automated robust CAD tool for accurate pediatric brain tumor volume segmentation and tracking over time. Note the current practice in brain tumor volume segmentation involves manual tracing and segmentation of suspected tumor areas in multimodality MRI which is time consuming, labor intensive, and may be imprecise. In an effort to reduce cognitive sequelae, contemporary protocols employ risk-adapted therapy in which risk stratification is based on volume of residual tumor after surgical resection and presence of metastatic disease at diagnosis. Therefore, further improvement in cancer treatment outcome in children is unlikely to be achieved without improved knowledge of tumor volume and classification among other factors. In addition, such automated volume computation and tracking tool would be of value as an adjunct marker in following up patients with brain tumors. This will, in turn, help the physicians to make important patient management decisions about surgery planning, critical radiation treatment planning modifications, treatment field modifications, localized control, sites of metastatic disease and post therapy response evaluation. However, development of such automated and precise tumor volume segmentation CAD tool requires solution to a few challenges such as detection of hard-to-detect brain tumor (small residual after surgery, poorly enhanced, multi foci and irregularly shaped) and abnormalities (edema, necrosis, and larger resection cavity due to surgery) detection and classification. This project aims at development, testing, and evaluation of innovative techniques and tools that will assist feature-based detection, segmentation and classification of brain tumor and a few specific abnormalities.} The specific aims of this project are: 1) Spline-multiresolution wavelet-fractal feature extraction; 2) MR sequence-dependant feature fusion and tumor/abnormality size and volume determination for improved detection; 3) Optimized feature fusion for improved tumor, tissue and abnormality classification; and 4) Algorithm testing and validation. {If successful, our method will allow for the automatic computation of brain tumors and abnormalities with improved accuracy, which can provide a rapid, objective, reproducible, and easily reported assessment of the disease. The results obtained from this project will have immediate impact in pediatric neuroradiology practice by providing an accurate, objective, and consistent way to evaluate and interpret brain tumors and associated abnormalities. PUBLIC HEALTH RELEVANCE: This project aims at development, testing, and evaluation of novel feature-based algorithms for robust, accurate and reproducible brain tumor and other abnormalities detection and classification. Such identification and classification will then be used to obtain precise segmentation of hard-to-detect brain tumors and abnormalities. We define hard-to-detect brain tumor as lesions that are small (residual after surgery), poorly enhanced, multi foci and irregularly shaped and abnormalities as edema, necrosis, and larger resection cavity due to surgery respectively. The algorithms capable of reliably and accurately computing segmented tumor volume would be of value as an adjunct marker in following up patients with brain tumors. Such a tumor volume quantification method would also have direct application in pre-clinical surgery planning and therapy trials leading to novel treatment strategies and devices. The results obtained from this project will have immediate impact in neuroradiology practice by providing an accurate, objective, and consistent way to evaluate and interpret brain tumors.