Computer Aided Liver Lesion Detection Algorithm

描述(由申请人提供)： 计算机辅助诊断(CAD)在医学成像领域的应用日益广泛。目前，计算机辅助设计主要用于乳房X光检查，在胸部成像方面的应用较少。肝脏成像是为了检查和评估肝脏肿瘤和其他可能扩散到肝脏的恶性肿瘤，这些肿瘤正在增长，并可能受到CAD的影响。肝脏计算机断层扫描(CT)是对恶性肿瘤患者进行肝脏筛查最常用的诊断方法。随着更高分辨率、更薄准直的CT扫描仪的出现，产生的图像数量越来越多，需要放射科医生进行解释。 在使用更薄的CT切片的同时，肝切除术也得到了改善，目前肝切除术被认为是孤立于肝脏的结直肠转移瘤的标准治疗方法。精确的肝脏病变的大小、数量和定位是帮助外科医生进行肝脏切除的术前计划所必需的。对于其他恶性肿瘤患者，最终治疗的选择在一定程度上取决于排除远处疾病，包括肝脏疾病。 在这项拟议的研究工作中，我们将开发用于肝脏CAD的工具，包括先进的计算机化方法，使用梯度向量流(GVF)蛇自动绘制肝脏轮廓，并使用改进的边缘图进行轮廓起始，通过局部密度最小算法对提取的肝脏中的转移进行计算机辅助检测，以及使用新的约束区域生长算法分割肝转移。 这项建议将利用145名转移性结直肠癌患者的数据库，这些患者以前曾参加过RO1资助的赠款“18F-氟代脱氧葡萄糖正电子发射断层扫描(PET)在肝脏结直肠转移患者术前评估中的应用”。这个数据库提供了一个将术前CT扫描与手术和病理结果相关联的绝佳机会，因此将成为开发和验证自动检测和分割肝转移的算法的理想试验台。开发一种成功的自动化肝损伤检测算法将大大提高检测效率。它有可能帮助放射科医生发现更多的病变，从而改善患者的分期。显然，这是发展进程的第一步，如果成功，将导致在自动化检测和定性方面的进一步努力。

DESCRIPTION (provided by applicant): The use of computer aided diagnosis (CAD) is growing in medical imaging. Currently CAD is used in mammography and to a lesser extent in chest imaging. Imaging of the liver is performed for the work-up and evaluation of hepatic neoplasms and other malignancies which may spread to the liver is growing and may be amenable to CAD. Computed Tomography (CT) on the liver is the most frequently used diagnostic modality to screen the liver in patients with malignancy. With higher resolution, thinner collimation CT scanners an increasing number of images are generated and need to be interpreted by Radiologists. Concurrent with the use of thinner CT sections, is the improvement in hepatic resection, which is currently considered the standard therapy for colorectal metastases isolated to the liver. Precise size, number and localization of liver lesions are necessary to aid the surgeon in preoperative planning for hepatic resection. In patients with other malignancies the choice of definitive therapy relies in part upon the exclusion of distant disease, including to the liver. In this proposed research work, we will develop tools for CAD of the liver including advanced computerized methods to automatically delineate liver contours using gradient vector flow (GVF) snake with a modified edge-map for the contour initiation, computer aided detection of metastases in the extracted livers by a local density minimum algorithm, and segmentation of liver metastases with a novel constrained region-growing algorithm. This proposal will capitalize on a database of 145 patients with metastatic colorectal cancer, who were previously enrolled in an RO1 funded grant "Utility of Whole-Body 18F-Fluorodeoxyglucose Positron Emission Tomography (PET) In the Preoperative Assessment of Patients with Hepatic Colorectal Metastases". This database provides an outstanding opportunity to correlate preoperative CT scanning with surgical and pathological findings, and will therefore serve as an ideal test bed for the development and verification of algorithms for the automatic detection and segmentation of liver metastases. The development of a successful automated liver lesion detection algorithm will greatly improve the efficiency of detectability. It has the potential to aid the radiologist in detecting more lesions and thus improve staging for patients. Clearly this is the first step in a process of development and if successful will lead to further efforts in automated detection and characterization.