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癌症，也是癌症死亡的第二大原因，仅次于肺癌。它起源于附着在结肠和直肠管腔壁上的小肿块（息肉）。如果息肉没有得到及时的诊断和治疗，它们可能会生长并变成癌症。未经治疗的结直肠癌从局部侵入的结肠和直肠（原位）扩散到周围组织、淋巴结（区域），并最终扩散到身体的远处，如肝脏和肺部。如果早期诊断，结直肠癌的治愈率显著，可达95%以上。因此，对于这种基本上可治愈的疾病，关键是早期诊断和治疗。美国癌症协会建议处于结直肠癌平均风险的人群在45岁时开始定期筛查。筛查结直肠癌的常用方法有四种：1)粪便隐血检查，即在粪便样本中检测不可见的血液；2)粪便免疫化学试验，检测粪便隐血；3)光学结肠镜检查，插入柔性内窥镜，目视检查直肠和结肠内壁；4)计算机断层扫描结肠术，使用从准备好的患者腹部CT扫描中获得的结肠3D重建模型，远程可视化结肠内部。计算机断层结肠镜检查（用于息肉的检测和分类）和光学结肠镜检查（用于息肉的确认和切除）的协调是检测和预防癌症的最佳选择。这个NSF-SCH项目涉及使用计算机断层扫描结肠镜作为结直肠癌的非侵入性早期筛查和随访，并将研究和创造方法来优化它并与光学结肠镜检查同步。从计算的角度来看，计算机断层扫描结肠镜包括五个步骤来分析患者准备的腹部CT扫描：1)图像处理（例如，电子结肠清洗）以纠正准备和扫描错误；2)图像分割，将结肠组织与腹部其他部分分离；3)三维重建，生成结肠的体积表示；4)将三维模型生成的腔面可视化，用于息肉的检测和评估；5)对检出息肉的位置、大小、形态及潜在病理进行分类分析。这个NSF SCH项目有三个目标：1)建立计算机断层扫描结肠镜系统的整个管道的分析方法，以增加研究人员在可视化步骤中已发表和专利的进展；2)开发新发现的Fly-In可视化方法的最佳实现，该方法使用虚拟摄像机在3D模型中导航，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结肠镜系统，该系统适合于基于人类和人工智能的读取本地和云上的大量计算机断层扫描结肠镜扫描，来自广泛分布的地理位置。需要探索和实现的创新包括：1)使用马尔可夫随机场和深度学习的结合，从准备好的患者的腹部CT扫描中自动分割结肠；2)采用可变形模型和离散优化对仰卧位和俯卧位CT扫描进行配准；3)对新发现的Fly-In可视化方法进行优化，包括用于可视化的虚拟摄像机数量、合适的管腔表面表示、2D CT扫描（轴向、矢状、冠状）和摄像机视场图像的交替投影，以及对管腔内息肉类型、大小和位置的区分；4)利用新颖的机器学习技术，在Fly-In方法中创建小型癌前息肉的最佳检测和分类算法；5)设计一个强大的基于云的计算机断层扫描结肠镜阅读系统，允许专家放射科医生在本地和云上阅读，使用知名专家的足够病例，以便提供计算机断层扫描结肠镜对结直肠癌诊断的可测量影响。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，并将为研究生和生物医学研究人员的培训提供肥沃的环境。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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