SBIR PHASE II TOPIC "Scalable Automated Brain Tumor Segmentation"

磁共振成像中的脑肿瘤分割是神经外科医生、肿瘤学家和神经外科医生的重要任务。 放射科医生评估疾病负担和测量肿瘤对治疗的反应。全球超过237，000人 估计有超过174，000人被诊断患有恶性脑和中枢神经系统疾病。在美国 仅在2015年，预计就有超过66，000例原发性恶性和非恶性脑和中枢神经系统肿瘤的新病例， 2014年确诊。准确定位和定向的脑肿瘤检测对于 有效的诊断、治疗计划和治疗效果分析;然而， 肿瘤需要相当长的时间，并且易于出错和变化很大。本提案的总体目标是 开发一种可扩展的自动化方法来分割脑肿瘤。该项目的目标是：1） 制作一个临床就绪的软件包，具有用户友好的图形用户界面，以管理大脑的过程 肿瘤分割和定量成像。2)实施生产软件模块，以准确检测和 从多通道MRI数据中对脑组织进行分类。3)支持定量成像、系统互操作性、结构化 报告，以及通过使用语义和注释标准的知识集成。4)证明 软件产生临床验证的结果，用于根据不同条件下大脑的MRI数据进行准确评估。 噪声条件、空间不均匀性、局部扫描仪设置和供应商设备。5)打包部署， 并测试SABTS工具用于临床实践，以准确检测，可视化和评估 脑肿瘤患者的疾病进展。

Brain tumor segmentation in Magnetic Resonance Imaging is an important task for neurosurgeons, oncologists, and radiologists to assess disease burden and measure tumor response to treatment. Over 237,000 individuals worldwide are estimated to have been diagnosed with malignant brain and CNS with over 174,000 deaths. In the United States alone, over 66,000 new cases of primary malignant and non-malignant brain and CNS tumors are expected to be diagnosed in 2014. Detection of brain tumors with the exact location and orientation is extremely important for effective diagnosis, treatment planning, and analysis of treatment effectiveness; however, manual delineation of the tumor takes considerable time and is prone to error and wide variability. The overall goal of this proposal is to develop a scalable and automated approach for the segmentation of brain tumors. The aims of the project are: 1) Produce a clinic ready software package with user-friendly graphical user interface to manage the process of brain tumor segmentation and quantitative imaging. 2) Implement the production software module to accurately detect and classify brain tissues from multi-channel MRI data. 3) Support quantitative imaging, system interoperability, structured reporting, and knowledge integration through the use of semantics and annotation standards. 4) Demonstrate the software produces clinically validated results for accurate assessment from MRI data of the brain under varying conditions of noise, spatial inhomogeneities, localized scanner settings and vendor equipment. 5) Package, deploy, and test the SABTS tools to be used in clinical practice for the accurate detection, visualization, and assessment of disease progression in patients with brain tumors.