AUTOMATIC COLON SEGMENTATION FOR 3D VIRTUAL COLONOSCOPY

一种成本效益高、无创且患者舒适的手术， 检测直径小于1cm的结肠息肉是极其困难的 对医疗保健很有价值。 由于结肠癌的进展是已知的， 息肉向恶性癌的转移，小息肉的检测和切除 完全可以治愈病人。目前可用的诊断利用 光学结肠镜检查虽然它是准确的，可以活检结肠 对于息肉，光学结肠镜检查具有几个缺点。 它很贵， 耗时，侵入性，需要静脉注射镇静剂， 穿孔风险小。 因此，对于大量患者来说， 筛选 为了克服这些缺点，我们一直在开发一种 创新技术，称为三维（3D）虚拟 结肠镜检查，并已证明其有效性成像息肉 直径小至3毫米。 在这项新技术中， 对患者的腹部进行断层扫描（CT），然后进行 计算机可视化系统用于在3D内虚拟导航 结肠模型寻找息肉。 这个项目的目标是 研究结肠分割和息肉的方法 识别. 有必要区分残余粪便和 息肉进行准确的结肠筛查。 具体目标是：（1） 研究对比剂用于粪便与 CT扫描上的结肠壁 适量的钡混合在 在CT扫描之前，将为患者提供膳食。 将混合物 钡剂与粪便混合后可增加粪便的图像密度。 最 然后自动标记粪便体素。(2)开发计算机 算法来分割残余粪便体素。 统计混合物 将研究模型以表征体素密度分布。 马尔可夫随机场先验将被调查，以模拟当地的 剩余粪便配置的结构。 最优贝叶斯 推理将用于计算识别残差的解决方案- 大便模式 (3)研究可视化技术， 实现交互式导航和区分残余粪便 从息肉。 摄像机控制和交互式表面和体积 将研究渲染以使医生能够检查结肠 表面和亚表面组织直观和交互式。 与 具体目标的成功，准确和具有成本效益的程序 进行大规模结肠筛查应该是可行的

A cost-effective, non-invasive, and patient-comfortable procedure for detecting colon polyps of a size less than 1 cm in diameter is extremely valuable for health care. Since there is a known progression of colonic polyps toward malignant carcinoma, detection and removal of small polyps can totally cure patients. Currently available diagnosis utilizes optical colonoscopy. Although it is accurate and can biopsy colonic polyps, optical colonoscopy has several drawbacks. It is expensive, time consuming, invasive, requires intravenous sedation and carries a small risk of perforation. Thus it is impractical for massive patient screening. To overcome these drawbacks, we have been developing an innovative technology, called three-dimensional (3D) Virtual Colonoscopy, and have demonstrated its effectiveness in imaging polyps as small as 3mm in diameter. In this new technology, a computed tomography (CT) scan of the patient's abdomen is taken and then a computer visualization system is used to virtually navigate within a 3D model of the colon searching for polyps. The goal of this project is to investigate the means for colon segmentation and polyp identification. It is necessary to differentiate residual stool from polyps for an accurate colon screening. The specific aims are: (1) To study contrast agents for a differentiated image contrast of stool from colon wall on the CT scans. An appropriate amount of barium mixed in meals will be given to the patients prior to the CT scans. The mixture of barium with stool will increase the image density of the stool. Most stool voxels will then be automatically labeled. (2) To develop computer algorithms to segment the residual-stool voxels. Statistical mixture models will be studied to characterize the voxel-density distribution. Markov random-field priors will be investigated to model the local structure of the residual-stool configuration. An optimal Bayesian inference will be used to compute the solution recognizing the residual- stool patterns. (3) To investigate visualization techniques for achieving interactive navigation and differentiating residual stools from polyps. Camera control and interactive surface and volume rendering will be studied to enable the physician to inspect the colonic surface and sub-surface tissue intuitively and interactively. With success of the specific aims, an accurate and cost-effective procedure for massive colon screening should be possible.