Sun Kyung Jeon
Seoul National University · 医学
研究室紹介
Professor Sun Kyung Jeon's research lab specializes in medical imaging and quantitative ultrasound, focusing on advancing non-invasive diagnostic techniques for abdominal diseases. The lab develops and validates novel ultrasound-based methods—particularly radiofrequency (RF) data analysis and deep learning algorithms—for accurate detection and characterization of hepatic steatosis and pancreatic tumors. Key research directions include improving the reproducibility and diagnostic performance of ultrasound attenuation imaging, integrating artificial intelligence with quantitative ultrasound parameters, and applying advanced segmentation algorithms to abdominal CT and MRI for precise organ and lesion analysis. The lab emphasizes clinical translation by using gold-standard imaging modalities like MRI-PDFF and surgical pathology as reference standards.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Purpose To determine useful magnetic resonance (MR) imaging features to differentiate nonhypervascular pancreatic neuroendocrine tumors (PNETs) from pancreatic ductal adenocarcinomas (PDACs). Materials and Methods The institutional review board approved this retrospective study and waived the informed consent requirement. Seventy-four patients with surgically confirmed PNETs and 82 patients with PDACs who underwent gadobutrol-enhanced MR imaging were included. Two radiologists independently eval
AC-TAI and SC-TSI derived from quantitative US RF data analysis yielded a good correlation with MRI-PDFF and provided good performance for detecting hepatic steatosis and assessing its severity in NAFLD.
Background Quantitative US (QUS) using radiofrequency data analysis has been recently introduced for noninvasive evaluation of hepatic steatosis. Deep learning algorithms may improve the diagnostic performance of QUS for hepatic steatosis. Purpose To evaluate a two-dimensional (2D) convolutional neural network (CNN) algorithm using QUS parametric maps and B-mode images for diagnosis of hepatic steatosis, with the MRI-derived proton density fat fraction (PDFF) as the reference standard, in patien
TSI-p and TAI-p derived from US RF data may be useful for detecting hepatic steatosis and assessing its severity.
PURPOSE: This study aimed to assess the inter-platform reproducibility of ultrasound attenuation examination in patients with nonalcoholic fatty liver disease (NAFLD). METHODS: Between March 2021 and April 2021, patients with clinically suspected or known NAFLD were prospectively enrolled; each patient underwent ultrasound attenuation examinations with three different platforms (Attenuation Imaging [ATI], Canon Medical System; Tissue Attenuation Imaging [TAI], Samsung Medison; and Ultrasound-Gui
A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous phase (PVP) and spatiotemporally-matched virtual non-contrast images, and tested using a single-energy (SE) CT dataset comprising PVP and true non-contrast (TNC) images. The algorithm showed robust accuracy in segmenting the liver, spleen, right kidne