• Journal of International Society for Simulation Surgery
• /
• 제3권1호
• /
• pp.28-32
• /
• 2016

Background Congenital muscular torticollis (CMT) is a benign condition. With early diagnosis and appropriate management, it can be cured completely, leaving no residual deformity. However, long-standing, untreated CMT can lead to permanent craniofacial deformities and asymmetry.Methods Nineteen patients presented to the author with congenital muscular torticollis. Three dimensional computed tomography (3-D CT) scans was obtained upon patient’s admission. Adjustment of skull’s position to Frankfort horizontal plan was done. Cervical vertebral segmentation was done which allowed a 3D module to be separately created for each vertebra to detect any anatomical or positional changes.Results The segmented vertebrae showed an apparent anatomical changes, which were most noticeable at the level of the atlas and axis vertebrae. These changes decreased gradually till reaching the seventh cervical vertebra, which appeared to be normal in all patients. The changes in the atlas vertebra were mostly due to its intimate relation with the skull base, while the changes of the axis were the most significantConclusion Cervical vertebral segmentation is a reliable tool for isolation and studying cervical vertebral pathological changes of each vertebra separately. The accuracy of the procedures in addition to the availability of many software that can be used for segmentation will allow many surgeons to use segmentation of the vertebrae for diagnosis and even for preoperative simulation planning.

• 보건행정학회지
• /
• 제12권1호
• /
• pp.125-143
• /
• 2002

The objectives of this study were analysis of patient\`s characteristics and market segmentation in oriental medical care and western medical care. This study focused on medical utilization using Anderson's health utilization model. The source of data was 1998 National Health and Nutrition Survey which Korean Institute For Health and Social Affairs carried out. A stratified multistage probability sampling design was used in this survey. The analysis was conducted using the statistical software package SPSS version 10.0 and Answer Tree 2.1 which is one of data mining methodology. The results were as follows ; 1) 44.9% of respondents reported visiting oriental medical center within recent two weeks. 3.4% of them used oriental medical care. The group of age, kind of disease and medical expenditure are associated with the difference western and oriental medical utilization rate. 2) There were several factors related to utilization of oriental medical care according to decision tree. Especially, important factors that patient chose his medical center were kinds of disease, kinds of common medical use, and expenditure. 3) in the results of CART analysis, market of oriental medical care were classified by seven categories. The major groups who have a preference for oriental medicine were those musculo-skeletal, cerebra-vascular disease, or chronic headache patients, and they had a preference fur oriental medical care in common use. These results show that oriental and western medical market were divided into various areas by market segmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권1호
• /
• pp.105-125
• /
• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

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

• 인터넷정보학회논문지
• /
• 제21권3호
• /
• pp.103-111
• /
• 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
• /
• 제28권6호
• /
• pp.791-797
• /
• 2007

This paper proposes a semiautomatic vertebra segmentation method that overcomes limitations of both manual segmentation requiring tedious user interactions and fully automatic segmentation that is sensitive to initial conditions. The proposed method extracts fence surfaces between vertebrae, and segments a vertebra using fence-limited region growing. A fence surface is generated by a deformable model utilizing valley information in a valley emphasized Gaussian image. Fence-limited region growing segments a vertebra using gray value homogeneity and fence surfaces acting as barriers. The proposed method has been applied to ten patient data sets, and produced promising results accurately and efficiently with minimal user interaction.

• International journal of advanced smart convergence
• /
• 제13권2호
• /
• pp.187-194
• /
• 2024

For precise cardiac diagnostics and treatment, we introduce a novel method for patient-specific mapping between myocardial and coronary anatomy, leveraging local variations in myocardial thickness. This complex system integrates and automates multiple sophisticated components, including left ventricle segmentation, myocardium segmentation, long-axis estimation, coronary artery tracking, and advanced geodesic Voronoi distance mapping. It meticulously accounts for variations in myocardial thickness and precisely delineates the boundaries between coronary territories according to the conventional 17-segment myocardial model. Each phase of the system provides a step-by-step approach to automate coronary artery mapping onto the myocardium. This innovative method promises to transform cardiac imaging by offering highly precise, automated, and patient-specific analyses, potentially enhancing the accuracy of diagnoses and the effectiveness of therapeutic interventions for various cardiac conditions.

• 대한의용생체공학회:의공학회지
• /
• 제42권5호
• /
• pp.201-210
• /
• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• 전자공학회논문지
• /
• 제50권8호
• /
• pp.225-231
• /
• 2013

의료영상분할은 다양한 의료영상처리를 수행하기에 앞서 먼저 수행되어야 하는 영상처리 기술이다. 그래서 빠르고 정확한 의료영상분할이 요구되고 있으며 다양한 의료영상분할 방법이 연구되고 있다. 의료영상에는 특성이 유사한 다양한 장기가 존재하기 때문에 분할영역의 정확한 판단이 필요하다. 그러나 의료영상은 장기의 일부가 작게 촬영되는 경우가 발생된다. 이 경우에는 분할영역을 판단하기 위한 정보가 부족하게 되며 그 결과 분할과정에서 분할영역이 제거된다. 본 논문에서는 볼륨 데이터와 선형 방정식을 이용하여 작은 영역에서의 분할결과를 개선하였다. 제안한 방법의 성능을 확인하기 위하여 흉부 CT 영상의 폐 분할을 수행하였다. 실험 결과, 의료영상의 분할 정확도는 0.978에서 0.981로 표준편차는 0.281에서 0.187로 개선되는 것을 확인하였다.

• 한국멀티미디어학회논문지
• /
• 제19권8호
• /
• pp.1236-1247
• /
• 2016

Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.