Chihua Fang

Outcomes of Hepatectomy for Hepatolithiasis Based on 3-Dimensional Reconstruction Technique

BACKGROUND The aim of our study was to evaluate the perioperative and long-term outcomes of hepatectomy based on 3-dimensional reconstruction technique for hepatolithiasis by comparing it with traditional hepatectomy. STUDY DESIGN From December 2005 to September 2012, 56 consecutive patients underwent hepatectomy based on 3-dimensional reconstruction technique for hepatolithiasis in our hospital (group A). During the same period, 42 patients with hepatolithiasis who met the inclusion criteria for hepatectomy were selected for traditional hepatectomy (group B). All operations were performed by the authors. There was no significant difference in preoperative data between the 2 groups statistically. RESULTS Compared with patients in group B, those in group A had a significantly lower stone residual rate (intermediate rate, 3.6% vs 19.0%; final rate, 0% vs 9.5%) and stone recurrence rate (3.6% vs 23.8%), a lower intrahepatic duct stricture residual rate (1.8% vs 14.3%), and a faster operating time (218.8 ± 55.5 minutes vs 254.7 ± 65.6 minutes). Intraoperative blood transfusion, intraoperative blood loss, postoperative hospital stay, and recurrent cholangitis rate were similar. No significant dominance was found in group A with respect to serum aminotransferase level, serum bilirubin level, serum albumin level, and prothrombin time. There was a significant dominance in group A for serum hemoglobin level (116.3 ± 16.0 g/L vs 108.0 ± 13.9 g/L; p < 0.05). Twenty-two complications occurred, 10 in group A and 12 in group B. Neither group had any perioperative mortality. CONCLUSIONS Hepatectomy for hepatolithiasis based on 3-dimensional reconstruction technique is feasible and safe in selected patients. Compared with traditional hepatectomy, it is more effective for diagnosis and treatment of hepatolithiasis.

Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation

In this paper, an automatic and robust coarse-to-fine liver image segmentation method is proposed. Multiple prior knowledge models are built to implement liver localization and segmentation: voxel-based AdaBoost classifier is trained to localize liver position robustly, shape and appearance models are constructed to fit liver these models to original CT volume. Free-form deformation is incorporated to improve the models’ ability of refining liver boundary. The method was submitted to VISCERAL big data challenge, and had been tested on IBSI 2014 challenge datasets and the result demonstrates that the proposed method is accurate and efficient.

To assess the benefits of medical image three‐dimensional visualization system assisted pancreaticoduodenctomy for patients with hepatic artery variance

Our main aim was to evaluate the value of medical image three‐dimensional visualization system (MI‐3DVS) in pancreaticoduodenctomy patients with hepatic artery variance.

Three-dimensional Reconstruction of the Peripancreatic Vascular System Based on Computed Tomographic Angiography Images and Its Clinical Application in the Surgical Management of Pancreatic Tumors

Objective This study aimed to investigate the clinical significance of 3-dimensional (3D) reconstruction of peripancreatic vessels for patients with suspected pancreatic cancer (PC). Methods A total of 89 patients with PC were included; 60 patients randomly underwent computed tomographic angiography. Based on the findings of 3D reconstruction of peripancreatic vessels, the appropriate method for individualized tumor resection was determined. These patients were compared with 29 conventionally treated patients with PC. Results The rate of visualization was 100% for great vessels around the pancreas. The detection rates for anterior superior pancreaticoduodenal artery, posterior superior pancreaticoduodenal artery, anterior inferior pancreaticoduodenal artery, posterior inferior pancreaticoduodenal artery, dorsal pancreatic artery, superior marginal arterial branch of the pancreatic head, anterior superior pancreaticoduodenal vein, posterior superior pancreaticoduodenal vein, anterior inferior pancreaticoduodenal vein, and posterior inferior pancreaticoduodenal vein were 86.6%, 85.0%, 76.6%, 71.6%, 91.6%, 53.3%, 61.6%, 55.0%, 43.3%, and 51.6%, respectively. Forty-three patients who had undergone 3D reconstruction underwent surgery. Of the 29 conventionally treated patients, 19 underwent surgery. The operative time, blood loss, length of hospital stay, and complication incidence of the 43 patients were superior to that of the 19 patients. Conclusions A peripancreatic vascular reconstruction can reveal the vascular anatomy, variations of peripancreatic vascular, and tumor-induced vascular changes; the application of the simulation surgery platform could reduce surgical trauma and decrease operative time.

Fast automatic 3D liver segmentation based on a three‐level AdaBoost‐guided active shape model

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-the-art automatic methods based on ASM.

A Kinect(™) camera based navigation system for percutaneous abdominal puncture.

Percutaneous abdominal puncture is a popular interventional method for the management of abdominal tumors. Image-guided puncture can help interventional radiologists improve targeting accuracy. The second generation of Kinect(™) was released recently, we developed an optical navigation system to investigate its feasibility for guiding percutaneous abdominal puncture, and compare its performance on needle insertion guidance with that of the first-generation Kinect(™). For physical-to-image registration in this system, two surfaces extracted from preoperative CT and intraoperative Kinect(™) depth images were matched using an iterative closest point (ICP) algorithm. A 2D shape image-based correspondence searching algorithm was proposed for generating a close initial position before ICP matching. Evaluation experiments were conducted on an abdominal phantom and six beagles in vivo. For phantom study, a two-factor experiment was designed to evaluate the effect of the operators skill and trajectory on target positioning error (TPE). A total of 36 needle punctures were tested on a Kinect(™) for Windows version 2 (Kinect(™) V2). The target registration error (TRE), user error, and TPE are 4.26  ±  1.94 mm, 2.92  ±  1.67 mm, and 5.23  ±  2.29 mm, respectively. No statistically significant differences in TPE regarding operators skill and trajectory are observed. Additionally, a Kinect(™) for Windows version 1 (Kinect(™) V1) was tested with 12 insertions, and the TRE evaluated with the Kinect(™) V1 is statistically significantly larger than that with the Kinect(™) V2. For the animal experiment, fifteen artificial liver tumors were inserted guided by the navigation system. The TPE was evaluated as 6.40  ±  2.72 mm, and its lateral and longitudinal component were 4.30  ±  2.51 mm and 3.80  ±  3.11 mm, respectively. This study demonstrates that the navigation accuracy of the proposed system is acceptable, and that the second generation Kinect(™)-based navigation is superior to the first-generation Kinect(™), and has potential of clinical application in percutaneous abdominal puncture.

Automatic Liver Segmentation Based on Shape Constrained Differeomorphic Demons Atlas Registration

Liver segmentation from abdominal Computer Tomography (CT) images plays an important role in liver disease diagnosis as well as liver surgical planning. In this paper, a hybrid approach is proposed for fully automatic liver position search and liver segmentation in CT images. First liver intensity range is detected based on prior knowledge of liver volume. Then region of interest (ROI) is extracted using atlas-based affine and non-rigid registration. At the last step, to achieve more accurate segmentation, major liver tumors are detected using gray level and distance prior knowledge, and then a modified diffeomorphic demons registration with shape constrain is applied. Thirty CT image datasets are tested, and the effectiveness is evaluated using volume mean overlay coefficient (Dice) and a comprehensive metric. Results show that our method can be a potential tool in clinical application.

Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches

This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p<0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p<0.001). The semiautomatic interactive 3DMIA, automatic ASM‐based, and automatic PA‐based liver volumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use. PACS number(s): 87.55.‐xThis study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%,-2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%,-3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p<0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p<0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volumetry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use. PACS number(s): 87.55.-x.

Quantitative analysis of gadoxetic acid-enhanced magnetic resonance imaging predicts histological grade of hepatocellular carcinoma

OBJECTIVE To confirm the histological grade of hepatocellular carcinoma (HCC) by gadoxetic acid-enhanced MRI. METHODS Ninety-five HCC patients underwent gadoxetic acid-enhanced MRI before surgical intervention. The correlations among the signal absolute enhancement, contrast enhancement ratio (CER) and tumor histological grade were analyzed. RESULTS The correlation between CER of tumor-to-liver and the grades of tumor differentiation is the most significant negative. The k-value for the CER of tumor-to-liver and histopathologic analysis is 0.62, which gives evidence of good agreement. CONCLUSION The quantitative analysis of gadoxetic acid-enhanced MRI can predict the histological grades of small HCCs.

Accuracy of actual resected liver volume in anatomical liver resections guided by 3-dimensional parenchymal staining using fusion indocyanine green fluorescence imaging: YANG et al.

The aim of this study was to assess the accuracy of actual resected liver volume (ARLV) in anatomical liver resections (ALRs) guided by 3‐dimensional parenchymal staining using fusion indocyanine green fluorescence imaging (IGFI).