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，也是仅次于肺癌的第二大癌症死亡原因。它起源于附着在结肠和直肠腔壁上的小生长物（息肉）。如果息肉没有及时诊断和治疗，它们可能会生长并癌变。未经治疗的结肠直肠癌从结肠和直肠的局部侵袭（原位）扩散到周围组织、淋巴结（区域性）并最终扩散到身体的远端部分，例如，肝脏和肺如果早期诊断，结直肠癌的治愈率非常高，达到95%以上。因此，对于这种在很大程度上可治愈的疾病，关键是早期诊断和治疗。美国癌症协会建议结直肠癌平均风险的人在45岁时开始定期筛查。有四种常见的方法来筛查结直肠癌：1）粪便潜血试验，检测粪便样本中不可见的血液; 2）粪便免疫化学试验，检测粪便中的潜血; 3）光学结肠镜检查，插入柔性内窥镜以目视检查直肠和结肠的内壁;以及4）计算机断层扫描结肠造影术，其使用来自准备好的患者的腹部CT扫描的结肠的3D重建模型来远程可视化结肠的内部。计算机断层扫描结肠成像（用于息肉检测和分类）和光学结肠镜检查（用于验证和切除息肉）的协调是检测和预防癌症的最佳选择。该NSF-SCH项目涉及使用计算机断层扫描结肠成像作为结直肠癌的非侵入性早期筛查和随访，并将研究和创建优化方法并与光学结肠镜同步。从计算的角度来看，计算机断层扫描结肠成像涉及五个步骤来分析患者准备的腹部CT扫描：1）图像处理（例如，电子结肠清洁）以校正准备和扫描仪错误; 2）图像分割以将结肠组织与腹部的其余部分隔离; 3）3D重建以生成结肠的体积表示; 4）由3D模型生成的腔表面的可视化以用于息肉检测和评估;以及5）分析以将检测到的息肉位置、大小、形状和潜在病理分类。这个NSF SCH项目有三个目标：1）为计算机断层扫描结肠成像系统的整个管道建立分析方法，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展; 2）开发新发现的Fly-In可视化方法的最佳实现，其使用虚拟摄像机的装备在3D模型内导航以实现专家和自动息肉检测;和3）开发一个前端计算机断层扫描结肠成像系统，适合人类和人工智能-基于从广泛分布的地理位置在本地和云上对大量计算机断层扫描结肠成像扫描的阅读。待探索和实施的创新包括：1）使用马尔可夫随机场和深度学习的组合，从准备好的患者的腹部CT扫描中自动分割结肠; 2）使用可变形模型和离散优化配准仰卧和俯卧CT扫描; 3）优化新发现的Fly-In可视化方法，在用于可视化的虚拟相机数量方面，管腔表面的正确表示，2D CT扫描的交替投影（轴向的、矢状的和冠状的）和在照相机装置的视场中的图像，以及关于类型、大小和在管腔中的位置的息肉的辨别; 4）使用新型机器学习技术在Fly-In方法中为小尺寸癌前息肉创建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成像阅读系统，该系统允许由放射科专家进行本地阅读，并在云上使用知名专家的足够病例，以提供一个衡量的影响计算机断层扫描结肠成像诊断结直肠癌。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NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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