• Investigative Magnetic Resonance Imaging
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• v.6 no.1
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• pp.47-54
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• 2002

Purpose : Hippocampal atrophy is one of the characteristic pathologic findings of hippocampal sclerosis, for which MR imaging of the hippocampus is essential in the evaluation of hippocampal sclerosis. The purpose of this study is to present the normal MR volumetric data of the hippocampus in normal adult Korean and to compare those with MR volumetric data of hippocampus in patients with hippocampal s-clerosis, providing the diagnostic volume criteria of the hippocampal atrophy. Materials and methods : MR volumetry was performed in 30 normal adults and 28 patients with temporal lobe epilepsy whose final diagnosis was hippocampal sclerosis. The volumetric data were compared between sexes, right and left sides, and normal and abnormal hippocampus, and the volume criteria for the diagnosis of hippocampal atrophy was determined. Results : The mean $volumes({\pm}standard$ deviation) of normal Korean adult were $2.20{\pm}0.73\textrm{cm}^3$ (right) and $2.17{\pm}0.72\textrm{cm}^3$ (left) in male and $2.27{\pm}0.47{\;}\textrm{cm}^3$ (right) and $2.23{\pm}0.48\textrm{cm}^3$ (left) in female. The mean right-left differences were $0.14{\pm}0.11\textrm{cm}^3$ and $0.19{\pm}0.13\textrm{cm}^3$ in male and female, respectively. The MR volumetry showed no significant statistical differences between sexes and between right and left. The mean volume and standard deviation of the hippocampus in hippocampal sclerosis patients was $1.46{\pm}0.60{\;}\textrm{cm}^3$, and the right-left difference was $0.51{\pm}0.41\textrm{cm}^3$, In comparison of two volume distributions between normal adult group and hippocampal sclerosis patients group, the reasonable diagnostic volume criteria was $0.4{\;}\textrm{cm}^3$ as right-left volume difference, in which the sensitivity and specificity are 0.61 and 0.90. In all patients with right-left volume difference more than $0.4{\;}\textrm{cm}^3$, visual determination of unilateral hippocampal atrophy was possible. Conclusion : The MR-based hippocampal volumetry is a useful add-on of visual MR diagnosis, only when visual diagnosis of hippocampal sclerosis is difficult.

• Journal of Magnetics
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• v.21 no.4
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• pp.603-615
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• 2016

In this study, in order to determine the validity and accuracy of MR imaging of 3D gradient dual echo 2-point DIXON technique for measuring abdominal adipose tissue volume and distribution, the measurements obtained by CT were set as a reference for comparison and their correlations were evaluated. CT and MRI scans were performed on each subject (17 healthy male volunteers who were fully informed about this study) to measure abdominal adipose tissue volume. Two skilled investigators individually observed the images acquired by CT and MRI in an independent environment, and directly separated the total volume using region-based thresholding segmentation method, and based on this, the total adipose tissue volume, subcutaneous adipose tissue volume and visceral adipose tissue volume were respectively measured. The correlation of the adipose tissue volume measurements with respect to the observer was examined using the Spearman test and the inter-observer agreement was evaluated using the intra-class correlation test. The correlation of the adipose tissue volume measurements by CT and MRI imaging methods was examined by simple regression analysis. In addition, using the Bland-Altman plot, the degree of agreement between the two imaging methods was evaluated. All of the statistical analysis results showed highly statistically significant correlation (p<0.05) respectively from the results of each adipose tissue volume measurements. In conclusion, MR abdominal adipose volumetry using the technique of 3D gradient dual echo 2-point DIXON showed a very high level of concordance even when compared with the adipose tissue measuring method using CT as reference.

• 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.

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.37-40
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• 2014

Purpose For living donor liver transplantation, liver segmentation is difficult due to the variability of its shape across patients and similarity of the density of neighbor organs such as heart, stomach, kidney, and spleen. In this paper, we propose an automatic segmentation of the liver using multi-planar anatomy and deformable surface model in portal phase of abdominal contrast-enhanced CT images. Method Our method is composed of four main steps. First, the optimal liver volume is extracted by positional information of pelvis and rib and by separating lungs and heart from CT images. Second, anisotropic diffusing filtering and adaptive thresholding are used to segment the initial liver volume. Third, morphological opening and connected component labeling are applied to multiple planes for removing neighbor organs. Finally, deformable surface model and probability summation map are performed to refine a posterior liver surface and missing left robe in previous step. Results All experimental datasets were acquired on ten living donors using a SIEMENS CT system. Each image had a matrix size of $512{\times}512$ pixels with in-plane resolutions ranging from 0.54 to 0.70 mm. The slice spacing was 2.0 mm and the number of images per scan ranged from 136 to 229. For accuracy evaluation, the average symmetric surface distance (ASD) and the volume overlap error (VE) between automatic segmentation and manual segmentation by two radiologists are calculated. The ASD was $0.26{\pm}0.12mm$ for manual1 versus automatic and $0.24{\pm}0.09mm$ for manual2 versus automatic while that of inter-radiologists was $0.23{\pm}0.05mm$. The VE was $0.86{\pm}0.45%$ for manual1 versus automatic and $0.73{\pm}0.33%$ for manaual2 versus automatic while that of inter-radiologist was $0.76{\pm}0.21%$. Conclusion Our method can be used for the liver volumetry for the pre-surgery planning of living donor liver transplantation.

• 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 ($149.17pages{\pm}55.92$).

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.124-131
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• 2001

복부 CT 영상에서 위의 자동적인 추출에 대하여 연구하였다. 복부 CT 영상에서 여러 장기가 비슷한 명암 값을 나타내며 분포 해 있다. 본 논문에서는 복부 CT 영상의 여러 장기 가운데 위를 자동적으로 추출하는 알고리즘을 개발하였다. 위는 움직이는 장기이며, 음식물로 채워진 부분과 공기로 채원진 부분으로 나뉘어져 있다. 이를 바탕으로 히스토그램 분석을 통한 명암 값 정보와 위치 정보를 이용하여 위를 탐색하고, 주변 다른 장기를 제거하는 다듬기 과정으로 완전한 위 추출 알고리즘을 완성하였다. 또한 돼지 실험에서 추출된 위의 체적을 비교하여, 개발된 알고리즘의 정확성을 검증한 결과 약 95%의 정확도를 보였다.

• Corrosion Science and Technology
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• v.16 no.3
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• pp.151-159
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• 2017

The MA8 alloy (formula Mg-Mn-Се) has been shown to have greater corrosion stability than the VMD10 magnesium alloy (formula Mg-Zn-Zr-Y) in chloride-containing solutions by Scanning Vibrating Electrode Technique (SVET) and by optical microscopy, gravimetry, and volumetry. It has been established that the crucial factor for the corrosion activity of these samples is the occurrence of microgalvanic coupling at the sample surface. The peculiarities of the kinetics and mechanism of the corrosion in the local heterogeneous regions of the magnesium alloy surface were investigated by localized electrochemical techniques. The stages of the corrosion process in artificial defects in the coating obtained by plasma electrolytic oxidation (PEO) at the surface of the MA8 magnesium alloy were also studied. The analysis of the experimental data enabled us to determine that the corrosion process in the defect zone develops predominantly at the magnesium/coating interface. Based on the measurements of the corrosion rate of the samples with PEO and composite polymer-containing coatings, the best anticorrosion properties were displayed by the composite polymer-containing coatings.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.473-485
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• 2022

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

• Korean Journal of Radiology
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• v.21 no.8
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• pp.987-997
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• 2020

Objective: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. Materials and Methods: A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. Results: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was -0.17 ± 3.07% for liver volume and -0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. Conclusion: The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.