• Korean Journal of Veterinary Research
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• v.38 no.4
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• pp.918-928
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• 1998

The present study was done with two aims. First, to evaluate the radiographic measurements of liver volumes in normal and hepatomegaly dogs induced by carbon tetrachloride. Second, to investigate quantitative tissue echo pattern by ultrasonography. Gray level histogram of the normal liver and the kidney were estimated with carbon tetra-chloride intoxication. In normal, r-square for liver volume to body weight was 0.93372, and this showed direct linear regression. Gray level histograms of the normal liver and the kidney were $19.150{\pm}2.490$(mean${\pm}$SD) and $13.175{\pm}2.686$(mean${\pm}$SD) respectively(p < 0.01). Liver parenchymal echogenicity was more hyperechogenic than kidney cortex echogenicity. Liver/Kidney ratio was $1.504{\pm}0.313$ and it can be used relative comparison of liver and kidney parenchymal echogenicity. In carbon-tetrachloride($CCl_4$) intoxication, changes of liver volume appeared to increase up to 24 hours after administration (p < 0.05), and decreased gradually to normal level after 2~5 days. Gray level histogram of liver parenchyma decreased up to 24hours (p < 0.01) after intoxication and then gradually increased to normal level. But that of kidney cortex had no significant change. Liver/Kidney ratio also decreased by 2 days(p < 0.01) and then gradually increased to normal level. On histopathologic features of hepatic tissues in carbon tetrachloride intoxication, both coagulative necrosis of hepatic cell and hemorrhage of centrilobular & midzonal area were identified. Conclusively, plain radiography is a useful diagnostic method for evaluating liver volume in mild hepatomegaly. Especially, it is considered that an adequate numerical processing of the liver length, depth and thoracic width and depth measurement would be helpful. Using gray level histogram, ultrasonographic evaluation was useful objective methods in early diagnosis of diffuse hepatic disease.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.1-9
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• 2014

Purpose : To analyze tumor's movement and volume change from changing position in order to minimize movement caused by breathing. Materials and Methods : We conducted survey of 14 patients with HCC(Hepatocellular carcinoma). Patient immobilization device was made in two ways(Supine position, prone position) and from image acquisition, tumor's movement, volume and dose are analyzed. Results : The mean movement of target(LR, Left-right) in supine position and prone position was $2.76{\pm}1.25mm$, $2.21{\pm}0.93mm$. AP(Anterior-posterior) and SI(Superior-inferior) was $4.02{\pm}1.63mm$, $11.56{\pm}3.08mm$, $3.36{\pm}1.17mm$, $7.45{\pm}1.96mm$. Treatment volume was decreased and normal liver volume was increased in prone position. Conclusion : We could reduce the margin of the treatment volume by minimizing the movement of liver caused by breathing. Especially in prone position, it is considered to be able to decrease the movement of the liver and increase normal liver volume.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.17-24
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• 2010

This paper proposed the method to separate a liver into left and right liver lobes for exact volumetry of the river graft at abdominal MDCT(Multi-Detector Computed Tomography) image before living donor liver transplantation. On the image of segmented liver, 4 points(the middle point of Inferior Vena Cava, a point of Middle Hepatic Vein, a point of Portal Vein, a middle point of gallbladder fossa) are selected. A liver is separated into left and right liver lobes on the basis of the 4 points. The volume and ratio of the river graft are estimated. The volume estimated using 4 points and the manual volume that radiologist processed and estimated are compared with the weight measured during surgery to support proof of the exact volumetry. After selection the 4 points, the time involved in separation a liver into left and right river lobe and volumetry of them is measured for confirmation that the algorithm can be used on real time during surgery. This study progressed to ensure donor's and recipient's safe who will undergo the liver transplantation.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• The Journal of Internal Korean Medicine
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• v.28 no.1
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• pp.115-123
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• 2007

Objectives : To investigate the correlations among alcohol consumption, alcoholic liver disorders, physical symptoms, and behaviors in heavy drinkers. Methods : 53 males who self-realized their severe alcohol consumption were enrolled in this study. 10 answers for a questionnaire, serum parameter, sonographic finding and body mass index were attained. The correlations between them were analyzed using Pearson's correlation and Student's t-test. Results : The average consumption of alcohol in these subjects was 2.5-fold over social drinkers. The incidence of alcoholic hepatitis was around 30%, while fatty liver 73%, and abnormal GGT 77%, respectively. No specific correlation between average volume of daily alcoholic consumption and alcohol-related hepatic parameters was shown in this study, but correlative tendency between fatty liver and body mass index was exhibited. Conclusions : This study may indicate that alcoholic liver injuries are caused by not just volume of alcohol consumed but more mixed factors including inherited genetic components, body fat mass, foods and other physical or emotional stress.

• 대한핵의학회:학술대회논문집
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• 1971.11a
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• pp.81.1-81.1
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• 1971

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.123-126
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• 2012

This paper proposed a method to separate a liver into left and right liver lobes for simple and exact volumetry of the river graft at abdominal MDCT(Multi-Detector Computed Tomography) image before the living donor liver transplantation. A medical team can evaluate an accurate river graft with minimized interaction between the team and a system using this algorithm for ensuring donor's and recipient's safe. On the image of segmented liver, 2 points(PMHV: a point in Middle Hepatic Vein and PPV: a point at the beginning of right branch of Portal Vein) are selected to separate a liver into left and right liver lobes. Middle hepatic vein is automatically segmented using PMHV, and the cutting line is decided on the basis of segmented Middle Hepatic Vein. A liver is separated on connecting the cutting line and PPV. The volume and ratio of the river graft are estimated. The volume estimated using 2 points are compared with a manual volume that diagnostic radiologist processed and estimated and the weight measured during surgery to support proof of exact volume. The mean ${\pm}$ standard deviation of the differences between the actual weights and the estimated volumes was $162.38cm^3{\pm}124.39$ in the case of manual segmentation and $107.69cm^3{\pm}97.24$ in the case of 2 points method. The correlation coefficient between the actual weight and the manually estimated volume is 0.79, and the correlation coefficient between the actual weight and the volume estimated using 2 points is 0.87. After selection the 2 points, the time involved in separation a liver into left and right river lobe and volumetry of them is measured for confirmation that the algorithm can be used on real time during surgery. The mean ${\pm}$ standard deviation of the process time is $57.28sec{\pm}32.81$ per 1 data set ($149.17pages{\pm}55.92$).

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.345-346
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• 2009

The segmentation of vessel including portal vein, hepatic vein and artery, from Computed Tomography (CT) images plays an important role in the therapeutic strategies for hepatic diseases. Representing segmented vessels in three dimensional spaces is extremely useful for doctors to plan liver surgery. In this paper, proposed method is focused on smoothing technique of segmented 3D liver vessels, which derived from 3D region growing approach. A pixel expand algorithm has been developed first to avoid vessel lose and disconnection cased by the next smoothing technique. And then a binary volume filtering technique has been implemented and applied to make the segmented binary vessel volume qualitatively smoother. This strategy uses an iterative relaxation process to extract isosurfaces from binary volumes while retaining anatomical structure and important features in the volume. Hard and irregular place in volume image has been eliminated as shown in the result part, which also demonstrated that proposed method is a suitable smoothing solution for post processing of fine vessel segmentation.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.757-768
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• 2022

In this paper, we proposed a system that visualizes a hologram device in 3D by utilizing the CT image segmentation function based on artificial intelligence deep learning. The input axial CT medical image is converted into Sagittal and Coronal, and the input image and the converted image are divided into 3D volumes using ResUNet, a deep learning model. In addition, the volume is created by segmenting the tumor region in the segmented liver image. Each result is integrated into one 3D volume, displayed in a medical image viewer, and converted into a video. When the converted video is transmitted to the hologram device and output from the device, a 3D image with a sense of space can be checked. As for the performance of the deep learning model, in Axial, the basic input image, DSC showed 95.0% performance in liver region segmentation and 67.5% in liver tumor region segmentation. If the system is applied to a real-world care environment, additional physical contact is not required, making it safer for patients to explain changes before and after surgery more easily. In addition, it will provide medical staff with information on liver and liver tumors necessary for treatment or surgery in a three-dimensional manner, and help patients manage them after surgery by comparing and observing the liver before and after liver resection.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.25 no.6
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• pp.481-488
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• 2022

Purpose: Liver transplantation (LT) is the only curative treatment for acute liver failure (ALF) and acute-on-chronic liver failure (ACLF). In high-volume therapeutic plasma exchange (HV-TPE), extracorporeal liver support filters accumulate toxins and improve the coagulation factor by replacing them. In this study, we aimed to evaluate the effectiveness of HV-TPE in pediatric patients with ALF and ACLF. Methods: We reviewed the records of children waiting for LT at Severance Hospital who underwent HV-TPE between 2017 and 2021. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), total and direct bilirubin (TB and DB), gamma-glutamyl transferase (GGT), ammonia, and coagulation parameter-international normalized ratio (INR) were all measured before and after HV-TPE to analyze the liver function. The statistical analysis was performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Co., Armonk, NY, USA). Results: Nine patients underwent HV-TPE with standard medical therapy while waiting for LT. One had neonatal hemochromatosis, four had biliary atresia, and the other four had ALF of unknown etiology. Significant decreases in AST, ALT, TB, DB, GGT, and INR were noted after performing HV-TPE (930.38-331.75 IU/L, 282.62-63.00 IU/L, 11.75-5.59 mg/dL, 8.10-3.66 mg/dL, 205.62-51.75 IU/L, and 3.57-1.50, respectively, p<0.05). All patients underwent LT, and two expired due to acute complications. Conclusion: HV-TPE could remove accumulated toxins and improve coagulation. Therefore, we conclude that HV-TPE can be regarded as a representative bridging therapy before LT.