• Journal of the Korean Society of Physical Medicine
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• v.13 no.3
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• pp.141-150
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• 2018

PURPOSE: This study was conducted to understand the adaptive responses of different modes of physical exercises utilizing skeletal muscle and the comprehensive relevance of AMPK signaling that can be activated by physical exercise as a potential molecular target in human health problems such as neuromuscular disorders (NMDs). METHODS: Most of the contents in this review article are based on recent publications concerning the main topics of interest. The reference literatures cited were obtained by basic searches of overseas academic databases such as PubMed and ScienceDirect using EndNote X7.8. RESULTS: The phenotypic adaptive responses of skeletal muscle during endurance- and resistance-based exercise training (ET and RT respectively) appear to be distinct. To explain the adaptive responses in each single mode of exercises (ET, RT) along with combined exercise training (CT), AMPK signaling is proposed as an important molecular link among those differential modes of exercise and a promising molecular target of NMDs. CONCLUSION: Based on the available evidence, intracellular AMPK signaling activated by diverse stimuli including physical exercise can be a potential and promising therapeutic target for the prevention, amelioration or cure of various human health problems including NMDs and may also be beneficial for physical rehabilitation and emergency situations that may elicit acute metabolic stresses.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.670-683
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• 2020

Objective: The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion: The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1042-1053
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• 2012

The purpose of this study was to identify the effects of Neurofeedback(NF), Cranio-Sacral Therapy(CST) and Combine Therapy(CT) in middle aged women through their experiences after participating these therapies. The participants were 53 middle aged women who lived in S city, 17 in the NF group, 17 in the CST and 19 in the CT, for 10 weeks from October to December, 2007. The NF group had 30 sessions, the CST group had 10 sessions and the CT group had 30 sessions of NF training after 10 sessions of CST. The data was collected from daily chart by self reporting their experiences during sessions. Collected data was analyzed by content analysis. From raw data, 37 items of NF, 91 items of CST and 110 items of CT were extracted in the content analysis. Similar items were gathered to 22 attributes of NF, 63 of CST and 68 of CT. These attributes were categorized into 9 higher attributes. The dominant attributes of NF were doziness during the training, mental comfort, lightening of physical and mental condition. Mental and physical comfort, improvement of sleep, healthy condition, crying were the dominant of CST. Also mental and physical comfort, lightening of physical condition, improvement of sleep, tear were the dominant of CT. According to the results of this study NF, CST and CT were very effective on physical and psychological relaxation. Therefore it is recommended that these NF, CST and CT be used as a complementary and alternative medicine(CAM) in middle aged women.

• Progress in Medical Physics
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• v.32 no.4
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• pp.172-178
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• 2021

Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility. Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics. Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant. Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.

• Progress in Medical Physics
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• v.23 no.1
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• pp.1-7
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• 2012

The purpose of this study was to develop the respiratory training system using individual characteristic guiding waveform to reduce the impact of respiratory motion that causes artifact in radiotherapy. In order to evaluate the improvement of respiratory regularity, 5 volunteers were included and their respiratory signals were acquired using the in-house developed belt-type sensor. Respiratory training system needs 10 free breathing cycles of each volunteer to make individual characteristic guiding waveform based on Fourier series and it guides patient's next breathing. For each volunteer, free breathing and guided breathing which uses individual characteristic guiding waveform were performed to acquire the respiratory cycles for 3 min. The root mean square error (RMSE) was computed to analyze improvement of respiratory regularity in period and displacement. It was found that respiratory regularity was improved by using respiratory training system. RMSE of guided breathing decreased up to 40% in displacement and 76% in period compared with free breathing. In conclusion, since the guiding waveform was easy to follow for the volunteers, the respiratory regularity was significantly improved by using in-house developed respiratory training system. So it would be helpful to improve accuracy and efficiency during 4D-RT, 4D-CT.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Korean Journal of Computational Design and Engineering
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• v.15 no.3
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• pp.234-242
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• 2010

3D CAD technology has been extended to a medical area including dental clinic beyond industrial design. The 2D images obtained by CT(Computerized Tomography) and MRI(Magnetic Resonance Imaging) are not intuitive, and thus the volume rendering technique, which transforms 2D data into 3D anatomic information, has been in practical use. This paper has focused on a method and its implementation for forming 3D geometric surface model from laminated CT images of the pubis. The implemented system could support a dental clinic to observe and examine the status of a patient's pubis before implant surgery. The supplement of 3D implant model would help dental surgeons settle operation plans more safely and confidently. It also would be utilized with teaching materials for a practice and training.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.991-1001
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• 2020

Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.