• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.425-429
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• 2008

We investigated algorithm to detect and characterize interstitial lung abnormalities seen at chest radiographs. This method includes a process of 4 directional Laplaction-Gaussian filtering, and a process of linear opacity judgment. Two regions of interest (ROIs) were selected in each right lung of patients, and these ROIs were processed by our computer-analyzing system. For quantitative analysis of interstitial opacities, the radiographic index, which is the percentage of opacity areas in a ROI, was obtained and evaluated in the images. From or result, abnormal lungs were well differentiated from normal lungs. In our algorithm, the processing results were not only given as the numeric data named "radiographic index" but also confirmed with radiologists observation on CRT. The approach, by which the interstitial abnormalities themselves are extracted, is good enough because the results can be confirmed by the observations of radiologists. In conclusion, our system is useful for the detection and characterization of interstitial lung abnormalities.

• Investigative Magnetic Resonance Imaging
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• v.20 no.1
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• pp.1-8
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• 2016

The spine is the most common location for skeletal metastases, and the incidence of spinal metastasis shows an increasing tendency. Because metastatic spinal tumors progress from an anterior element to a posterior element resulting in continuing destruction of the pedicles, epidural extension and involvement of neural structures of the metastatic tumor are eventually visible. Therefore, it is clinically significant for radiologists to understand the pathophysiology of spinal metastasis and to assess the involvement of neural structures and the disintegration of spinal instability related to the pathophysiology. As MRI is also the best imaging modality for diagnosing spinal metastasis, radiologists should accurately assess spinal metastasis and provide practical information to physicians. Therefore, we will describe some analysis points focusing on the understanding of pathophysiology of spinal metastasis and the next step toward a more extensive clinical approach using MR imaging.

• Journal of Biomedical Engineering Research
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• v.26 no.6
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• pp.407-412
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• 2005

Convenience is one of the most important factors in medical image segmentation. Convenience is defined by compiling opinions from radiologists, and can be described as controllable maximum automation on the condition of producing only accurate results. The components of convenience are inclusive automation and inclusive modification. Inclusive modification consists of verify-and-confirm, undo-redo, exchange of segmentation methods, and intelligent modification tools. Inclusive automation is composed of automatic selection of a method, automatic selection of a confident segment, and automated chores. The convenient segmentation tool has been developed to segment X-ray images for orthopedic surgery, and has received an excellent evaluation from radiologists.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.454-463
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• 2021

Interstitial lung abnormalities (ILAs) are radiologic abnormalities found incidentally on chest CT that are potentially related to interstitial lung diseases. Several articles have reported that ILAs are associated with increased mortality, and they can show radiologic progression. With the increased recognition of ILAs on CT, the role of radiologists in reporting them is critical. This review aims to discuss the clinical significance and radiologic characteristics of ILAs to facilitate and enhance their management.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.33-41
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• 2020

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years. Many articles on deep learning have been published in radiologic journals. However, radiologists may have difficulty in understanding and interpreting these studies because the study methods of deep learning differ from those of traditional radiology. This review article aims to explain the concepts and terms that are frequently used in deep learning radiology articles, facilitating general radiologists' understanding.

• Korean Journal of Radiology
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• v.23 no.2
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• pp.202-217
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• 2022

Pulmonary arteriovenous malformation (AVM) is a congenital vascular disease in which interventional radiologists can play both diagnostic and therapeutic roles in patient management. The diagnosis of pulmonary AVM is simple and can usually be made based on CT images. Endovascular treatment, that is, selective embolization of the pulmonary artery feeding the nidus of the pulmonary AVM, and/or selectively either the nidus or draining vein, has become a first-line treatment with advances in interventional devices. However, some vascular diseases can simulate pulmonary AVMs on CT and pulmonary angiography. This subset can confuse interventional radiologists and referring physicians. Vascular mimickers of pulmonary AVM have not been widely known and described in detail in the literature, although some of these require surgical correction, while others require regular follow-up. This article reviews the clinical and radiologic features of pulmonary AVMs and their mimickers.

• Korean Journal of Radiology
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• v.20 no.1
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• pp.69-82
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• 2019

In 2017, the American Joint Committee on Cancer announced the 8th edition of its cancer staging system. For breast cancer, the most significant change in the staging system is the incorporation of biomarkers into the anatomic staging to create prognostic stages. Different prognostic stages are assigned to tumors with the same anatomic stages according to the tumor grade, hormone receptor (estrogen receptor; progesterone receptor) status, and HER2 status. A Clinical Prognostic Stage is assigned to all patients regardless of the type of therapy used; in contrast, a Pathologic Prognosis Stage is assigned to patients in whom surgery is the initial treatment. In a few situations, low Oncotype DX recurrence scores can change the prognostic stage. The radiologists need to understand the importance of the biologic factors that can influence cancer staging.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.604-607
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• 2020

• Korean Journal of Radiology
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• v.23 no.5
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• pp.570-570
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• 2022

• Korean Journal of Radiology
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• v.21 no.6
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• pp.773-776
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• 2020