Computer Aided Liver Lesion Detection Algorithm

描述（由申请人提供）： 计算机辅助诊断（CAD）在医学成像中的应用越来越多。目前，CAD用于乳房X线摄影，在较小程度上用于胸部成像。肝脏成像用于检查和评估肝肿瘤和其他可能扩散到肝脏的恶性肿瘤，这些肿瘤正在生长并可能适合CAD。肝脏计算机断层扫描（CT）是最常用的诊断方式，以筛选肝脏恶性肿瘤患者。随着更高分辨率、更薄准直CT扫描仪的出现，越来越多的图像被生成并需要由放射科医师进行解释。 在使用更薄的CT切片的同时，肝切除术也得到了改进，这是目前被认为是孤立于肝脏的结直肠转移瘤的标准治疗方法。准确的肝脏病变的大小，数量和定位是必要的，以帮助外科医生在术前计划肝切除术。在其他恶性肿瘤患者中，确定性治疗的选择部分依赖于排除远处疾病，包括肝脏。 在这项拟议的研究工作中，我们将开发工具CAD的肝脏，包括先进的计算机化的方法，自动描绘肝脏轮廓使用梯度矢量流（GVF）蛇与修改后的边缘地图的轮廓启动，计算机辅助检测的转移，在提取的肝脏局部密度最小值算法，和分割的肝脏转移瘤与一种新的约束区域生长算法。 该提案将利用145名转移性结直肠癌患者的数据库，这些患者之前参加了RO 1资助的赠款“全身18F-氟脱氧葡萄糖正电子发射断层扫描（PET）在肝结直肠转移患者术前评估中的应用”。该数据库提供了一个很好的机会，术前CT扫描与手术和病理结果相关联，因此，将作为一个理想的测试床的自动检测和分割肝转移瘤的算法的开发和验证。一个成功的自动化肝脏病变检测算法的发展将大大提高检测效率。它有可能帮助放射科医生检测更多的病变，从而改善患者的分期。显然，这是开发过程中的第一步，如果成功，将导致在自动检测和表征方面的进一步努力。

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.