Texture-Based CAD for Cancer Screening from 3D Images

描述(申请人提供)：结直肠癌是美国第三种最常见的癌症，也是第二大癌症死亡原因。膀胱癌是美国癌症死亡的第五大原因。通常，癌症在患者出现症状后被诊断为晚期，这解释了他们的高死亡率。由于大多数结肠癌是在5到15年的恶变期内由息肉引起的，所以人们提倡对直径小于1厘米的小息肉进行筛查。不幸的是，大多数人没有遵循这一建议。膀胱癌的分期也有类似的情况。该项目与健康相关的是通过提供一种方便的、几乎无风险的程序来显著增加愿意参与筛查计划的人数。虚拟结肠镜(VCON)和虚拟膀胱镜(VCys)作为对患者腹部进行CT或MR成像，并使用计算机可视化系统在构建的结肠或膀胱的3D模型中进行虚拟导航的新方法，已经显示出在结肠癌筛查和膀胱肿瘤评估中的潜力。多年来，我们一直在为VCON和VCys的开发做出贡献。该项目广泛的长期目标是开发基于3D纹理的计算机辅助检测(TxCAD)技术，以促进VCON和VCys作为准确、经济、非侵入性、舒适的技术来筛查大部分人群。第一阶段的具体目标是：(1)发展组织混合图像分割，以减轻部分体积效应。(2)提取黏膜层，清除切除的中空器官的管腔。(3)以高度敏感性和合理的假阴性确定粘膜层的可疑斑块。(4)获取每个可疑补丁的全部体积。第二阶段的目标是：(5)从每个可疑的体积中提取3D几何、形态和纹理信息。(6)利用学习机对提取的特征进行分类，消除误报。(7)评估建议的txCAD在超过200个病例的患者数据库上的性能。与以前的基于几何的CAD(SfCAD)相比，提出的txCAD将显著提高检测性能，并显著减少医生与我们开发的VCON和VCys系统的交互时间。

DESCRIPTION (provided by applicant): Colorectal carcinoma is the third most commonly diagnosed cancer and the second leading cause of death from cancer in the United States. Bladder cancer is the fifth cause of cancer deaths in the United States. Often the cancers are diagnosed at an advanced stage after the patients have developed symptoms, explaining their high mortality rates. Since most colon cancers arise from polyps over a 5 to 15 year period of malignant transformation, screening programs to detect small polyps less than 1 cm in diameter have been advocated. Unfortunately most people do not follow this recommendation. A similar situation exists for bladder tumor staging. The health relatedness of this project is to dramatically increase the number of people willing to participate in screening programs by offering a convenient, nearly risk-free procedure. Virtual colonoscopy (VCon) and virtual cystoscopy (VCys), as new procedures in which computed tomographic (CT) or magnetic resonance (MR) images of the patient's abdomen are taken and a computer visualization system is used to virtually navigate within a constructed 3D model of the colon or bladder, has demonstrated the potential for colon cancer screening and bladder tumor evaluation. We have been contributing the VCon and VCys development for several years. The broad, long-term objective of this project is to develop 3D texture-based computer aided detection (txCAD) techniques to facilitate VCon and VCys as accurate, cost-effective, non-invasive, comfortable techniques to screen large segments of the population. The Phase I specific aims are: (1) To develop tissue-mixture image segmentation mitigating the partial volume effect. (2) To extract the mucosa layer and cleanse the lumen space of the hollow organs from the segmentation. (3) To identify suspicious patches in the mucosa layer with high sensitivity and reasonable false negatives. (4) To obtain the entire volume of each suspicious patch. The Phase II aims are: (5) To extract 3D geometrical, morphological and texture information from each suspicious volume. (6) To classify the extracted features using learning machine to eliminate false positives. (7) To evaluate the performance of the proposed txCAD on a patient database of over 200 cases. It is hypothesized that the proposed txCAD will significantly improve the detection performance compared to previous geometry-based CAD (sfCAD), and dramatically reduces the physician's interaction time with our developed VCon and VCys systems.