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Anatomical variations of the right hepatic vein, especially large variant right hepatic veins (≥5 mm), have important clinical implications in liver transplantation and resection. This study aimed to evaluate anatomical variations of the right hepatic vein using quantitative three‐dimensional visualization analysis. Computed tomography images of 650 patients were retrospectively analyzed, and three‐dimensional visualization was applied using the derived data to analyze large variant right hepatic veins. The proportion of the large variant right hepatic vein was 16.92% (110/650). According to the location and number of the variant right hepatic veins, the configuration of the right hepatic venous system was divided into seven subtypes. The length of the retrohepatic inferior vena cava had a positive correlation with the diameter of the right hepatic vein (rs = 0.266, p = 0.001) and the variant right hepatic veins (rs = 0.211, p = 0.027). The diameter of the right hepatic vein was positively correlated with that of the middle hepatic vein (rs = 0.361, p < 0.001), while it was inversely correlated with that of the variant right hepatic veins (rs = −0.267, p = 0.005). The right hepatic vein diameter was positively correlated with the drainage volume (rs = 0.489, p < 0.001), while the correlation with the variant right hepatic veins drainage volume was negative (rs = −0.460, p < 0.001). The number of the variant right hepatic veins and their relative diameters were positively correlated (p < 0.001). The volume and percentage of the drainage area of the right hepatic vein decreased significantly as the number of the variant right hepatic vein increased (p < 0.001). The findings of this study concerning the variations of the hepatic venous system may be useful for the surgical planning of liver resection or transplantation. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC), the fifth most common cancer worldwide, is increasing nowadays and poses a serious threat to human health. However, if treated effectively and timely, it is clinically manageable or curable. Therefore, accurate detection and complete surgical resection remain priorities for HCC with a high potential of improving both survival and quality of life. Lacking of real-time guide technology, traditional surgery are usually relied on the subjective experience of surgeon, which have the limitation of high sensitivity detection tumor. Here, we developed a contrast agent, ZnGa 2 O 4 Cr 0.00 4 (ZGC), used for guided surgery during operation to accurate delineation of HCC. ZGC showed excellent long-lasting afterglow properties that lasted for hours, which can aid in real-time guided surgery. Meanwhile, ZGC display high spatial resolution and deep penetration during pre-operation for diagnostic computed tomography (CT). Interestingly, we observed reverse imaging in the tumor region, known as a “dark hole”, which further improves the contrast for surgery. This new multi-modality nanoparticle has great potential for accurate liver cancer imaging and resection guidance. [ABSTRACT FROM AUTHOR]

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods--3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration--are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-theart automatic methods based on ASM. [ABSTRACT FROM AUTHOR]