• Journal of Gastric Cancer
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• 제11권1호
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• pp.64-68
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• 2011

A 54 year old man was referred to our hospital with gastric cancer. The patient had a history of splenectomy and a left nephrectomy as a result of a traffic accident 15 years earlier. The endoscopic findings were advanced gastric cancer at the lower body of the stomach. Abdominal ultrasonography (USG) and magnetic resonance imaging demonstrated a metastatic nodule in the S2 segment of the liver. Eventually, the clinical stage was determined to be cT2cN1cM1 and a radical distal gastrectomy, lateral segmentectomy of the liver were performed. The histopathology findings confirmed the diagnosis of intrahepatic splenosis, omental splenosis. Hepatic splenosis is not rare in patients with a history of splenic trauma or splenectomy. Nevertheless, this is the first report describing a patient with gastric cancer and intrahepatic splenosis that was misinterpreted as a liver metastatic nodule. Intra-operative USG guided fine needle aspiration should be considered to avoid unnecessary liver resections in patients with a suspicious hepatic metastasis.

• 한국콘텐츠학회논문지
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• 제5권5호
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• pp.305-305
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• 2005

현대사회에 있어서 질병은 매우 다양하게 발견되고 있다. 또한 한번 발병하면 아니면 적절한 검진시기를 놓쳐 돌이킬 수 없는 상황을 접하게 되는 경우가 종종 있다. 사회가 발전하면서 과도한 비즈니스와 음주 흡연 등으로 우리의 간은 매우 피곤한 상태에 놓이게 되며 질병 발생율도 매우 높다. 여러 질병 중 특히 간에 관련된 질병은 회복이 불가하여 장기이식수술에 의존하는 편이다. 이와 관련하여 본 연구는 MDCT로 얻어진 영상을 3차원 영상 기법으로 간의 형상을 렌더링 하여 체적을 구하고 간 이식 수술시 혈관과의 분리 작업을 통해 절개 라인을 결정할 수 있도록 미리 시물레이션 할 수 있는 영상처리 기법을 개발하고자 간 영역 자동 추출 알고리즘을 적용하여 시물레이션 한 결과 수술을 용이하게 집도할 수 있도록 새로운 장을 열게 되었다.

• Journal of International Society for Simulation Surgery
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• 제1권1호
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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.

• Anatomy and Cell Biology
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• 제56권4호
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• pp.579-583
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• 2023

In human fetuses, the left hepatic artery (LHA) issues the marginal artery that runs along the umbilical vein and, sometimes, reaches the umbilicus. The further observation demonstrated that, in 5 of 12 Japanese midterm fetuses (crown-rump length mm: 46, 50, 54, 59, 102), the marginal artery issued not only a thin umbilical branch but also a liver parenchymal branch that took a posterosuperior recurrent course in a peritoneal fold and supplied the anterior surface of the liver left lobe (segment III). However, in 22 Spanish fetuses of which gestational ages corresponded to the Japanese ones, we did not find the parenchymal branch. Therefore, between human populations, there seemed to be a considerable difference in the incidence as to whether or not the marginal artery issues the liver parenchymal branch. The parenchymal branch might be degenerated at the later stages due to friction between the liver free surface and growing diaphragm.

• Nuclear Medicine and Molecular Imaging
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• 제42권3호
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• pp.253-255
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• 2008

A 61-year-old man had a F-18 FDG PET/CT scan for evaluation of a common bile duct cancer identified on CT. The PET/CT image showed a hypermetabolic mass in the common bile duct, and a focal area of increased F-18 FDG uptake in segment IV of the liver, which corresponded to a hypoattenuated lesion on non-enhanced CT, and was consistent with hepatic metastasis. The patient underwent choledochojejunostomy with hepatic resection, and pathologic findings were compatible with an eosinophilic abscess in the liver. This case demonstrates that F-18 FDG uptake by an eosinophilic abscess can mimic hepatic metastasis in a patient with a malignancy.

• 인터넷정보학회논문지
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• 제21권3호
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• pp.103-111
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• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.

• 대한의용생체공학회:의공학회지
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• 제43권5호
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Journal of Chest Surgery
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• 제49권6호
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• pp.481-484
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• 2016

A 53-year-old man arrived at the trauma center with a steel bar penetrating from the epigastrium to the right scapula. He was hypotensive and hypoxic, and immediate resuscitation and basic evaluation were performed. An emergency operation was performed due to an unstable hemodynamic state. Multiple injuries were confirmed in the right lower lobe, posterior chest wall, diaphragm, and liver lateral segment. Right lower lobectomy and liver lateral sectionectomy were performed following removal of the bar. The patient recovered without additional hemorrhage after the surgery, and was transferred to a rehabilitation institution with periodic follow-up.

• Parasites, Hosts and Diseases
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• 제61권3호
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• pp.292-297
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• 2023

Extra-gastrointestinal anisakidosis is rare. We herein report an Anisakis pegreffii infection in a patient with hepatic anisakidosis diagnosed based on its molecular identification. A 71-year-old male patient had a hepatic tumor presenting as a low-density area of 20 mm in diameter in segment 6 of the liver on abdominal ultrasonography, computed tomography, and magnetic resonance imaging. The surgically resected pathological specimen revealed a necrotizing eosinophilic granuloma containing nematode larvae, possibly an Anisakis larva. Molecular and phylogenetic analysis demonstrated Anisakis larvae belonging to A. pegreffii. The present results will help identify and characterize unknown Anisakis species in histological sections.

• Parasites, Hosts and Diseases
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• 제50권4호
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• pp.357-360
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• 2012

A 25-year-old Uzbek male presented with right upper abdominal pain for 20 days. On radiologic studies, a huge cystic mass was noticed in the right liver which was suspected as parasitic. The patient received right hepatic segmentectomy (segment 7), and the surgically resected mass was confirmed as cystic echinococcosis (CE), measuring 10.5 cm in its diameter. The inner surface of the cyst was bile-stained. The patient was discharged on the 8th hospital day, and was rechecked 6 months after the surgical intervention without any evidence of recurrence. The present report describes findings of an imported case of CE which represented ultrasound images of the 'ball of wool'.