SCH: Robust CT Colonography for Local & Cloud-Based Screening

SCH：本地稳健 CT 结肠成像

• 批准号: 2124316
• 负责人: Aly Farag
• 金额: $ 105万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124316&HistoricalAwards=false
• 关键词: SCH; Robust; CT; Colonography; Local

Colorectal cancer is the third most common cancer in the U.S. It is also the second leading cause of cancer deaths, behind lung cancer. It originates as small growths (polyps) attached to the luminal wall of the colon and rectum. If polyps are not timely diagnosed and treated, they may grow and become cancerous. Untreated colorectal cancer spreads from local invasion of the colon and rectum (in situ) into surrounding tissues, lymph nodes (regional) and eventually to distant parts of the body, e.g., the liver and lungs. If diagnosed early, colorectal cancer has a remarkable recovery rate, reaching over 95%. Therefore, the key for this largely curable disease is early diagnosis and treatment. The American Cancer Society recommends that people at average risk of colorectal cancer start regular screening at age 45. There are four common methods to screen for colorectal cancer: 1) Fecal occult blood test, which detects blood in a stool sample that is not visible; 2) A fecal immunochemical test, which detects occult blood in stool; 3) Optical Colonoscopy, where a flexible endoscope is inserted to visually inspect the interior walls of the rectum and colon; and 4) Computed Tomography Colonography, which remotely visualizes the interior of the colon using a 3D reconstructed model of the colon from an abdominal CT scan of prepped patients. Coordination of Computed Tomography Colonography (for polyps detection and classification) and Optical Colonoscopy (for validation and removal of polyps) holds the best option to detect and prevent cancer. This NSF-SCH project deals with using Computed Tomography Colonography as a non-invasive early screening and follow-up for colorectal cancer, and would research and create methods for optimizing it and synchronization with Optical Colonoscopy. From a computational perspective, Computed Tomography Colonography involves five steps to analyze a patient-prepped abdominal CT scan: 1) image processing (e.g., Electronic Colon Cleansing) to correct prep and scanner errors; 2) image segmentation to isolate the colon tissue from the rest of the abdomen; 3) 3D reconstruction to generate a volumetric representation of the colon; 4) visualization of the luminal surface generated by the 3D model for polyp detection and assessment; and 5) analysis to catalog detected polyps location, size, shape, and potential pathology. This NSF SCH project has three goals: 1) Establish an analytic approach for the entire pipeline of the Computed Tomography Colonography system, to augment the published and patented progress made by the investigators in the visualization step; 2) Develop an optimal implementation of the newly discovered Fly-In visualization approach, which uses a rig of virtual cameras to navigate inside the 3D model to enable expert and automatic polyp detection; and 3) Develop a front-end Computed Tomography Colonography system that lends itself to human and artificial intelligence-based reading of massively large number of Computed Tomography Colonography scans locally and on the cloud, from widely distributed geographical locations. Novelties to be explored and implemented include: 1) use a combination of Markov Random Field and Deep Learning for automatic segmentation of the colon from the abdominal CT scan of prepped patients; 2) Registration of supine and prone CT scans using deformable models and discrete optimization; 3) Optimization of the newly discovered Fly-In visualization method, in terms of the number of virtual cameras used for visualization, proper representation of the lumen surface, alternating projections of 2D CT scans (axial, sagittal and coronal) and images in the field of view of the camera rig, and discrimination of polyps with respect to type, size and location in the lumen; 4) Create optimal detection and classification algorithms for small-size precancerous polyps in the Fly-In approach using novel machine learning techniques; 5) Design a robust cloud-based Computed Tomography Colonography reading system which allows for local reading by expert radiologists and on the cloud, using sufficient cases by renowned experts, in order to provide a measured impact of Computed Tomography Colonography for diagnosis of colorectal cancer. These tasks include novel theoretical and computational methods, collaboration of a large multidisciplinary team, and will provide a fertile environment for training of graduate students and biomedical researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

结直肠癌是美国第三大常见的癌症，它也是癌症死亡的第二大原因，仅次于肺癌。它起源于结肠和直肠腔壁附着的小生长（息肉）。如果不及时诊断和治疗息肉，它们可能会成长并变成癌。未经治疗的结直肠癌从结肠和直肠（原位）的局部入侵进入周围的组织，淋巴结（区域），最终传播到人体远处的部分，例如肝脏和肺部。如果早期诊断出，结直肠癌的恢复率显着，达到95％以上。因此，这种在很大程度上可以治愈的疾病的关键是早期诊断和治疗。美国癌症协会建议，有大肠癌的平均风险的人们在45岁时开始定期筛查。有四种筛查结直肠癌的常见方法：1）粪便隐匿血液测试，检测到粪便样本中不可见的血液； 2）粪便免疫化学检测，检测粪便中的神秘血； 3）光学结肠镜检查，插入柔性内窥镜以视觉检查直肠和结肠的内壁； 4）计算机断层扫描结肠造影，该结肠造影术，使用预备患者的腹部CT扫描中的结肠的3D重建模型远程可视化结肠内部。计算机断层扫描（用于息肉检测和分类）和光学结肠镜检查（用于息肉的验证和去除）的协调是检测和预防癌症的最佳选择。该NSF-SCH项目介绍了使用计算机断层扫描作为无创的早期筛查和结直肠癌的随访，并将研究并创建用于优化IT并与光学结肠镜检查同步的方法。从计算的角度来看，计算机断层扫描仪涉及五个步骤来分析患者对患者的腹部CT扫描：1）图像处理（例如电子结肠清洁）以纠正准备和扫描仪错误； 2）图像分割以将结肠组织与腹部其余部分分离； 3）3D重建产生结肠的体积表示； 4）3D模型为息肉检测和评估产生的腔表面的可视化； 5）分析对息肉的分析位置，大小，形状和潜在病理。该NSF SCH项目具有三个目标：1）为计算机断层扫描系统的整个管道建立一种分析方法，以增强研究人员在可视化步骤中所取得的已发表和专利进度； 2）开发新发现的飞出可视化方法的最佳实现，该方法使用虚拟摄像机在3D模型内导航，以实现专家和自动息肉检测； 3）开发一个前端计算机断层扫描系统，该系统适合于人类和人工智能的基于大量计算机断层扫描扫描本地和云上的大量读物，从广泛分布的地理位置。要探索和实施的新颖性包括：1）使用马尔可夫随机领域和深度学习的组合来自动分割预备患者的腹部CT扫描； 2）使用可变形模型和离散优化对仰卧和俯卧扫描进行注册； 3）根据用于可视化的虚拟摄像头的数量，适当表示管腔表面的适当表示，2D CT扫描（轴向，矢状和冠状动脉）以及相机钻机视野中的2D CT扫描（轴向，矢状和冠状动脉）的交替投影，以及相对于类型，大小和位置的位置，以相对于摄像机钻机的视野，在摄像机的视野中，对新发现的飞出可视化方法进行了优化，以可视化的虚拟相机数量，适当表示。 4）使用新颖的机器学习技术，在飞行方法中为小型癌前息肉创建最佳检测和分类算法； 5）设计一种强大的基于云的计算机层析成像造影术阅读系统，它允许专家放射科医生和云上的本地阅读，使用著名专家的足够情况，以提供计算机断层扫描造影术对诊断结直肠癌的测量影响。这些任务包括新颖的理论和计算方法，大型多学科团队的合作，并将为培训研究生和生物医学研究人员提供肥沃的环境。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准来通过评估来获得支持的。

项目成果

An Automatic Colorectal Polyps Detection Approach for Ct Colonography

• DOI: 10.1109/icip49359.2023.10221981
• 发表时间: 2023-10
• 期刊: 2023 IEEE International Conference on Image Processing (ICIP)
• 作者: Mohamed Yousuf;Islam Alkabbany;Asem A. Ali;Salwa Elshazley;Albert Seow;Gerald W. Dryden;Aly A. Farag