Sang Joon Park
Seoul National University · Medicine
About the Lab
Professor Sang Joon Park's research lab specializes in medical image analysis and computational radiology, focusing on leveraging advanced imaging techniques such as MRI, CT, and 3D printing to improve diagnostic accuracy and surgical planning in neuro- and oncological diseases. The lab emphasizes the development of deep learning-based automated segmentation tools for accurate tumor volume measurement, particularly in meningiomas and gliomas, and applies radiomics and texture analysis to predict tumor behavior and metastatic potential. The integration of patient-specific 3D-printed models into clinical workflows further enhances surgical simulation and personalized treatment planning.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15A texture analysis of the ADC map based on the entire tumor volume can be useful for evaluating glioma grade, which provides tumor heterogeneity.
Computed tomography texture features such as higher entropy and lower homogeneity were significant differentiating factors of IPAs presenting as PGGNs larger than 5 mm and have potentials to enhance the differentiating performance.
The usefulness of 3-dimensional (3D)-printed disease models has been recognized in various medical fields. This study aims to introduce a production platform for patient-specific 3D-printed brain tumor model in clinical practice and evaluate its effectiveness. A full-cycle platform was created for the clinical application of a 3D-printed brain tumor model (3D-printed model) production system. Essential elements included automated segmentation software, cloud-based interactive communication tools
BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice. PURPOSE: To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning. STUDY TYPE: Ret
AIMS: Metabolic syndrome (MS) is a global health problem associated with an increased risk of diabetes mellitus (DM), cardiovascular disease (CVD), and cancer. Body composition parameters, including obesity, visceral adiposity, and sarcopenia contribute to the development of MS and CVD. Previous studies have investigated the association of individual body composition parameters with MS. Studies analyzing the association between multiple body composition parameters and MS have been rare. We aimed
Pulmonary metastases from osteosarcoma could be differentiated from non-metastases by using computerized texture analysis. Higher mean attenuation and larger diameter were significant predictors for pulmonary metastases, while higher mean attenuation was a significant predictor for small non-calcified pulmonary metastases.
OBJECTIVES: The aim of this study was to describe the temporal changes of various texture features extracted from pulmonary nodules on dynamic contrast-enhanced computed tomography (DCE-CT) and to compare the feature values among multiple scanning time points. We also aimed to analyze the variability of texture features across multiple scan delay times. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board of Seoul National University Hospital with waiver
The evaluation of proptosis is essential for the diagnosis of orbital disease. We have developed a computed tomography (CT)-based three-dimensional (3D) reconstruction software to measure the degree of proptosis. To verify clinical usefulness and reliability, the degree of proptosis was measured in 126 patients with Graves' orbitopathy (GO) with 3D reconstruction software and compared with those obtained with Hertel exophthalmometer and CT. The proptosis values measured by 3D reconstruction soft
승용차 부문의 에너지수요 또는 대기오염물 배출에 대해 영향을 미치는 정책의 효과를 정확히 예측하기 위해서는 소비자의 승용차 보유 및 이용행태에 대한 분석 및 예측이 선행되어야 한다. 이를 위해 본 연구는 네스티드로짓모형을 활용한 가계의 자동차 보유대수 및 차종선택에 대한 연구로서 2003년 에너지경제연구원에서 수행한 에너지간이조사 자료를 이용하여 추정을 시도하였다. 모형의 추정결과를 요약하면, 모형의 적합도(goodness of fit)를 나타내는 값과 포괄값이 모두 통계적으로 유의한 범위 내에 있어 네스티드로짓모형을 활용한 방법이 더 우수하다고 할 수 있다. 또한 운행비용의 탄력성이 1보다 커서 가구의 차량보유대수 및 차종선택은 운행비용에 대해 매우 민감하다는 것과 소득계층별로는 저소득 계층이 고소득 계층보다 더 민감하게 반응한다는 것을 알 수 있었다.
목적: 미용콘택트렌즈와 관련된 합병증을 보고하고자 한다. 증례요약: 미용콘택트렌즈와 연관된 합병증으로 내원한 9명의 환자의 의무기록을 후향적으로 조사하여 보고하였다. 9명의 환자에서 각막미란이 모두 발생하였으며, 2명의 환자에서 각막상피결손이 발생하였다. 3명에서 각막윤부에서 2 mm 이상 자라 들어간 각막신생 혈관이 관찰되었으며 이 중 1명은 양안의 영구적인 시력저하가 발생하였다. 기존에 다른 콘택트렌즈를 사용하던 2명을 제외한 7명이 콘택트렌즈 관리에 대한 교육을 제대로 받지 못하였다. 9명 모두가 자신이 사용하고 있는 미용콘택트렌즈의 제조회사와 제품에 대한 정보를 제공받지 못하였다. 미용콘택트렌즈의 사용을 중지하거나, 적절한 사용법에 대한 교육을 시행한 후 경과 관찰하였다. 경과 관찰 된 6명 중 5명에서는 각막미란 등의 합병증이 모두 호전되었다. 결론: 미용콘택트렌즈를 사용하는 사람은 적절한 관리에 대한 교육을 받지 못하는 경우가 많다. 콘택트렌즈 사용 전후로 적절한 교육 이
Hypothyroidism is a recognized sequela of conventional thyroid lobectomy. However, there have been no studies on the incidence of hypothyroidism following the preservation of the isthmus and pyramid during lobectomy. Therefore, in the present study, we compared the incidence of hypothyroidism following conventional lobectomy and lobectomy during which the isthmus and pyramidal lobe were preserved. Data for a total of 65 patients collected between September 2018 and April 2019 were reviewed retro
Research Areas
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