• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1960-1969
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• 2016

In this paper, we propose an automatic hierarchical organ segmentation method on abdominal CT images. First, similar atlases are selected using bone-based similarity registration and similarity of liver, kidney, and pancreas area. Second, each abdominal organ is roughly segmented using image-based similarity registration and intensity-based locally weighted voting. Finally, the segmented abdominal organ is refined using mask-based affine registration and intensity-based locally weighted voting. Especially, gallbladder and pancreas are hierarchically refined using location information of neighbor organs such as liver, left kidney and spleen. Our method was tested on a dataset of 12 portal-venous phase CT data. The average DSC of total organs was $90.47{\pm}1.70%$. Our method can be used for patient-specific abdominal organ segmentation for rehearsal of laparoscopic surgery.

• Korean Journal of Radiology
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• v.23 no.7
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• pp.720-731
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• 2022

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV_BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LV_BSA, and aLSSR × LV_BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LV_BSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.634-637
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• 2002

오늘날 CT나 MR등을 통한 의학 영상 기술과 컴퓨터 성능의 향상으로 인체 내부 장기의 영상을 비교적 용이하게 얻을 수 있으며 얻어진 영상 정보는 컴퓨터로 수치와 되므로 데이터의 조작 및 가공이 용이하다. 그러나, 이 데이터는 2D 슬라이스들의 연속으로 표현되므로 이것을 보다 편리하게 가시화. 조작, 분석이 용이한 상태로 바꾸기 위해서는 3차원 구조로의 재구성이 필요하게 된다. 이것을 위하여 무엇보다도 먼저 CT나 MR을 통하여 얻어진 영상을 분석하여 특정 장기의 영상 부분를 다른 조직의 영상부분으로부터 분리(segmentation)할 필요가 있다. 이러한 Segmentation방법에는 여러가지가 있는데, 수작업의 결합 등으로 인해서 비효율적인 문제점을 가지고 있다. 이에 본 논문은 보다 효율적인 segmentation의 처리를 위하여 region-based 기법을 응용하여 새로운 segmentation 방법을 개발하였다. 그리하여, 본 논문이 제안한 알고리즘을 슬라이스 간격이 큰 2차원 복부 CT 영상에 적용시켜 간(liver)의 추출을 시도하였고 향상된 성능을 확인할 수 있었다.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.430-437
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• 2010

It is essential for living donor liver transplantation that surgeon must understand the hepatic vessel structure to improve the success rate of operation. In this paper, we extract the liver boundary without other surrounding structures such as heart, stomach, and spleen using the contrast enhanced MDCT liver image sequence. After that, we extract the major hepatic veins (left, middle, right hepatic vein) with morphological filter after review the basic structure of hepatic vessel which reside in segmented liver image region. The purpose of this study is provide the overall status of transplantation operation with size estimation of resection part which is dissected along with the middle hepatic vein. The method of liver extraction is as follows: firstly, we get rid of background and muscle layer with gray level distribution ratio from sampling process. secondly, the coincident images match with unit mesh image are unified with resulted image using the corse coordinate of liver and body. thirdly, we extract the final liver image after expanding and region filling. Using the segmented liver images, we extract the hepatic vessels with morphological filter and reversed the major hepatic vessels only with a results of ascending order of vessel size. The 3D reconstructed views of hepatic vessel are generated after applying the interpolation to provide the smooth view. These 3D view are used to estimate the dissection line after identify the middle hepatic vein. Finally, the volume of resection region is calculated and we can identify the possibility of successful transplantation operation.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.434-440
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• 2022

Liver transplantation is a critical used treatment method for patients with end-stage liver disease. The number of cases of living donor liver transplantation is increasing due to the imbalance in needs and supplies for brain-dead organ donation. As a result, the importance of the accuracy of the donor's suitability evaluation is also increasing rapidly. To measure the donor's liver volume accurately is the most important, that is absolutely necessary for the recipient's postoperative progress and the donor's safety. Therefore, we propose liver segmentation in abdominal CT images from pre-operation, POD 7, and POD 63 with a two-dimensional U-Net. In addition, we introduce an algorithm to measure the volume of the segmented liver and measure the hepatectomy rate and regeneration rate of pre-operation, POD 7, and POD 63. The performance for the learning model shows the best results in the images from pre-operation. Each dataset from pre-operation, POD 7, and POD 63 has the DSC of 94.55 ± 9.24%, 88.40 ± 18.01%, and 90.64 ± 14.35%. The mean of the measured liver volumes by trained model are 1423.44 ± 270.17 ml in pre-operation, 842.99 ± 190.95 ml in POD 7, and 1048.32 ± 201.02 ml in POD 63. The donor's hepatectomy rate is an average of 39.68 ± 13.06%, and the regeneration rate in POD 63 is an average of 14.78 ± 14.07%.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.763-766
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• 2002

오늘날 CT나 MR 등을 통한 의학 영상 기술과 컴퓨터 성능의 향상으로 인체 내부 장기의 영상을 비교적 용이하게 얻을 수 있으며 얻어진 영상 정보는 컴퓨터로 수치화되므로 데이터의 조작 및 가공 또한 용이하다. 그러나, 이 데이터는 2D 슬라이스(slice)들의 연속으로 표현되므로 이것을 보다 가시화, 조작, 분석이 용이한 상태로 바꾸기 위해서는 3 차원 구조로의 재구성이 필요하게 된다. 이것을 위하여 무엇보다도 먼저 CT 나 MR 을 통하여 얻어진 영상을 분석하여 특정장기(organ)의 영상 부분을 다른 조직의 영상부분으로부터 분리(segmentation)할 필요가 있다. 이러한 Segmentation방법에는 여러가지가 있는데, 수작업의 결합 등으로 인해서 비효율적일 수 밖에 없는 문제점을 가지고 있다. 이에 본 논문은 보다 효율적인 segmentation 의 처리를 위하여 구역확장(region-growing) 기법을 응용한 새로운 segmentation 방법을 개발하였다. 그리하여, 본 논문이 제안한 알고리즘을 슬라이스 간격이 큰 2 차원 복부 CT 영상에 적용시켜 간(liver)의 추출을 시도하였고 3차원 표현 결과를 확인할 수 있었다.

• Progress in Medical Physics
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• v.24 no.2
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• pp.99-107
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• 2013

The surgical resection was occurred mainly in liver metastasis before the development of radiation therapy techniques. Recently, Radiation therapy is increased gradually due to the development of radiation dose delivery techniques. 18F-FDG PET image showed better sensitivity and specificity in liver metastasis detection. This image modality is important in the radiation treatment with planning CT for tumor delineation. In this study, we applied automatic image segmentation methods on PET image of liver metastasis and examined the impact of image factors on these methods. We selected the patients who were received the radiation therapy and 18F-FDG PET/CT in Korea Cancer Center Hospital from 2009 to 2012. Then, three kinds of image segmentation methods had been applied; The relative threshold method, the Gradient method and the region growing method. Based on these results, we performed statistical analysis in two directions. 1. comparison of GTV and image segmentation results. 2. performance of regression analysis for relation between image factor affecting image segmentation techniques. The mean volume of GTV was $60.9{\pm}65.9$ cc and the $GTV_{40%}$ was $22.43{\pm}35.27$ cc, and the $GTV_{50%}$ was $10.11{\pm}17.92$ cc, the $GTV_{RG}$ was $32.89{\pm}36.8$4 cc, the $GTV_{GD}$ was $30.34{\pm}35.77$ cc, respectively. The most similar segmentation method with the GTV result was the region growing method. For the quantitative analysis of the image factors which influenced on the region growing method, we used the standardized coefficient ${\beta}$, factors affecting the region growing method show GTV, $TumorSUV_{MAX/MIN}$, $SUV_{max}$, TBR in order. The result of the region growing (automatic segmentation) method showed the most similar result with the CT based GTV and the region growing method was affected by image factors. If we define the tumor volume by the auto image segmentation method which reflect the PET image parameters, more accurate and consistent tumor contouring can be done. And we can irradiate the optimized radiation dose to the cancer, ultimately.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1765-1772
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• 2012

Success rate of transplantation of body organs improved due to development of medical equipment and diagnostic technology. In particular, a liver transplant due to liver dysfunction has increased. With the development of image processing and analysis to obtain the volume for liver transplantation have increased the accuracy and efficiency. In this thesis, we try to reconstruct the regions of the liver within three dimensional images using the mevislab tool, which is effective in quick comparison and analysis of various algorithms, and in expedient development of prototypes. Liver is divided by applying threshold values and region growing method to the original image, and by removing noise and unnecessary entities through morphology and region filling, and setting of areas of interest. It is deemed that high temporal efficiency, and presentation of diverse range of comparison and analysis module application methods through usage of MeVisLab would make contribution towards expanding of baseline of medical image processing researches.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.51-57
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• 2012

Hepatic vessel tree is the key structure for hepatic disease diagnosis and liver surgery planning. Especially, it is used to evaluate the donors' and recipients' liver for the LDLT(Living Donors Liver Transplantation) and estimate the volumes of left and right hepatic lobes for securing their life in the LDLT. In this study, we propose a method to apply canny edge detection that is not affected by noise to the liver images for automatic segmentation of hepatic vessels tree in contrast abdominal MDCT image. Using histograms and average pixel values of the various liver CT images, optimized parameters of the Canny algorithm are determined. It is more time-efficient to use the common parameters than to change parameters manually according to CT images. Candidates of hepatic vessels are extracted by threshold filtering around the detected the vessel edge. Finally, using a system which detects the true-negatives and the false-positives in horizontal and vertical direction, the true-negatives are added in candidate of hepatic vessels and the false-positives are removed. As a result of the process, the various hepatic vessel trees of patients are accurately reconstructed in 3D.

• Smart Media Journal
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• v.5 no.1
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• pp.49-54
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• 2016

Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.