Computer-Aided Detection of Hepatocellular Carcinoma in Multiphase Contrast-Enhanced Hepatic CT: A Preliminary Study

摘要

Malignant liver tumors such as hepatocellular carcinoma (HCC) account for 1.25 million deaths each year worldwide. Early detection of HCC is sometimes difficult on CT images because the attenuation of HCC is often similar to that of normal liver parenchyma. Our purpose was to develop computer-aided detection (CADe) of HCC using both arterial phase (AP) and portal-venous phase (PVP) of contrast-enhanced CT images. Our scheme consisted of liver segmentation, tumor candidate detection, feature extraction and selection, and classification of the candidates as HCC or non-lesions. We used a 3D geodesic-active-contour model coupled with a level-set algorithm to segment the liver. Both hyper- and hypo-dense tumors were enhanced by a sigmoid filter. A gradient-magnitude filter followed by a watershed algorithm was applied to the tumor-enhanced images for segmenting closed-contour regions as HCC candidates. Seventy-five morphologic and texture features were extracted from the segmented candidate regions in both AP and PVP images. To select most discriminant features for classification, we developed a sequential forward floating feature selection method directly coupled with a support vector machine (SVM) classifier. The initial CADe before the classification achieved a 100% (23/23) sensitivity with 33.7 (775/23) false positives (FPs) per patient. The SVM with four selected features removed 96.5% (748/775) of the FPs without any removal of the HCCs in a leave-one-lesion-out cross-validation test; thus, a 100% sensitivity with 1.2 FPs per patient was achieved, whereas CADe using AP alone produced 6.4 (147/23) FPs per patient at the same sensitivity level.