박창민 교수
Chang Min Park
서울대학교 영상의학과 · 의학
연구실 소개
박창민 교수의 연구실은 의료 영상 진단의 정밀도와 효율성을 높이기 위한 인공지능 기반 영상 분석 기술을 핵심으로 연구를 진행하고 있습니다. 특히 흉부 X-ray와 CT 영상에서 폐암, 흉부 질환, 결핵 등의 조기 진단을 위한 딥러닝 기반 자동 진단 알고리즘 개발에 주력하고 있으며, 임상 현장에서의 적용 가능성을 높이기 위해 다양한 영상 재구성 알고리즘과의 상호작용 및 판독자 간 변동성 분석도 함께 수행하고 있습니다. 이는 진단의 객관성과 일관성을 제고하는 데 기여합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15The algorithm consistently outperformed physicians, including thoracic radiologists, in the discrimination of chest radiographs with major thoracic diseases, demonstrating its potential to improve the quality and efficiency of clinical practice.
The popularization of computed tomography (CT) in clinical practice and the introduction of mass screening for early lung cancer with the use of CT have increased the frequency of findings of subtle nodules or nodular ground-glass opacity. Nodular ground-glass opacity may be observed in malignancies such as bronchioloalveolar carcinoma and adenocarcinoma, as well as in their putative precursors, such as atypical adenomatous hyperplasia. Nodular ground-glass opacity also may be seen in the presen
BACKGROUND: Detection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An automated system may help streamline the tuberculosis screening process and improve diagnostic performance. METHODS: We developed a deep learning-based automatic detection (DLAD) algorithm using 54c221 normal CRs and 6768 CRs with active pulmonary tuberculosis that were labeled and annotated by 13 board-certified radiologists. The performance of DLAD w
OBJECTIVE: To determine the incidence and etiologies of celiac axis stenosis in asymptomatic individuals. MATERIALS AND METHODS: This prospective study involved 400 consecutive patients (male: 319, female: 81) referred to us for celiac arteriography between April and July 1999. When celiac axis branches were opacified by collateral circulation during superior mesenteric arteriography, the presence of celiac axis stenosis was suspected; lateral projection celiac arteriography was performed and th
A variety of tumors, including primary malignant tumors, secondary malignant tumors, and benign tumors, can occur in the tracheobronchial tree. Primary malignant tumors commonly originate from the surface epithelium or the salivary glands, whereas most benign tumors arise from the mesenchymal tissue. At computed tomography (CT), primary malignant tumors manifest as a polypoid lesion, a focal sessile lesion, eccentric narrowing of the airway lumen, or circumferential wall thickening. At fluorine
PURPOSE: To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. METHODS: Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two re
Computed tomography texture features have the potential to be used as prognostic biomarkers in unresectable NSCLC patients undergoing definitive CCRT.
In 2011, the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society proposed a new classification for lung adenocarcinoma that included a number of changes to previous classifications. This classification now considers resection specimens, small biopsies, and cytology specimens. Two former histopathologic terms, bronchioloalveolar carcinoma and mixed subtype adenocarcinoma, are no longer to be used. For resection specimens, the new ter
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.
Atypical adenomatous hyperplasia is often associated with malignancy. This tumor is shown on CT as persistent well-defined oval or round nodular GGOs without solid components, and it does not change on the follow-up CT.
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