박정환 교수

This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.

Nonalcoholic fatty liver disease, characterized by excessive accumulation of fat in the liver, is the most common chronic liver disease worldwide. The current standard for the detection of hepatic steatosis is liver biopsy; however, it is limited by invasiveness and sampling errors. Accordingly, MR spectroscopy and proton density fat fraction obtained with MRI have been accepted as non-invasive modalities for quantifying hepatic steatosis. Recently, various quantitative ultrasonography technique

CTTA was demonstrated to provide texture features significantly correlated with higher tumor grade as well as predictive markers of DFS after surgical resection of HCCs in addition to other valuable imaging and clinico-pathologic parameters.

Hepatocellular carcinoma (HCC) is a unique cancer entity that can be noninvasively diagnosed using imaging modalities without pathologic confirmation. In 2018, several major guidelines for HCC were updated to include hepatobiliary contrast agent magnetic resonance imaging (HBA-MRI) and contrast-enhanced ultrasound (CEUS) as major imaging modalities for HCC diagnosis. HBA-MRI enables the achievement of high sensitivity in HCC detection using the hepatobiliary phase (HBP). CEUS is another imaging

이 연구의 목적은 한국 도시의 건축물 노후도 및 리모델링 현황을 조사하고 그 특성을 분석하는 것이다. 이를 위해 전국 252개 지자체 단위로 노후건축물 수, 리모델링 추세를 조사하였으며, t-검정과 상관분석을 통해 노후건축물 밀집도와 리모델링 사이의 관계를 살펴보았다. 분석 결과, 리모델링이 시급한 건축물 노후 지역에서 오히려 리모델링이 활성화되고 있지 않음을 확인하였다. 이는 대규모 재개발이 힘든 지역에서 건축물 단위로 도시환경을 개선하는 수단인 리모델링기법이 아직 효과를 보지 못하고 있음을 의미한다. 또한, 2000년대 이후 리모델링에 대한 관심이고조되고, 리모델링 기술 또한 발전하였으나, 아직 리모델링 활성화를 위한 제도 지원은 미비한것으로 파악되었다. 특히, 지금까지 도입된 관련 정책들의 경우 대부분 공동주택 중심의 제도들이라는 점에서 한계가 있는 것으로 분석되었다. 향후 도시의 노후도를 개선하기 위한 수단으로서노후건축물 밀집 지역을 중심으로 공공에 의한 맞춤형 리모델링 지원

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

• Automated organ segmentation enables the extraction of CT attenuation-based parameters for the target organ. • Volumetric liver and spleen CT attenuation-based parameters are highly accurate in hepatic steatosis assessment. • Automated CT measurements from pre- or post-contrast imaging show promise for hepatic steatosis screening in large cohorts.

This study aimed to evaluate inspiratory lung expansion in patients with interstitial lung disease (ILD) using histogram analyses based on advanced image registration between inspiratory and expiratory CT scans. We included 16 female ILD patients and eight age- and sex-matched normal controls who underwent full-inspiratory and expiratory CT scans. The CT scans were sequentially aligned based on the surface, landmarks, and attenuation of the lung parenchyma. Histogram analyses were performed on t

Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.