Sejung Yang
연세대학교 정밀의학과 · 의학
Sejung Yang 교수의 연구실은 의료 영상 및 표면 분석 분야에서 신호 처리와 이미지 분석 기반의 정밀 진단 기술을 연구하고 있습니다. 특히 광학 현미경, FLIM(형광 수명 영상), PAES(양성자-붕괴 유도 아우어 전자 스펙트로스코피) 등을 활용한 낮은 신호 대비 비율과 신호 의존성 노이즈를 가진 이미지의 정량적 분석에 중점을 두고 있으며, 딥러닝 기반 세포 및 세포 소견(예: 골격세포)의 자동 분할 및 추적 기술도 개발하고 있습니다. 연구는 주로 생체 영상의 정밀도 향상과 비침습적 진단 기술의 실현을 목표로 하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probabi
Preserving hue is an important issue for colour image enhancement. Here, a hue‐preserving gamut mapping method with high saturation is proposed. Experimental results with the Macbeth colour chart and natural images show vivid colour with higher subjective image quality.
Fluorescence lifetime imaging microscopy (FLIM) is a microscopic imaging technique to present an image of fluorophore lifetimes. It circumvents the problems of typical imaging methods such as intensity attenuation from depth since a lifetime is independent of the excitation intensity or fluorophore concentration. The lifetime is estimated from the time sequence of photon counts observed with signal-dependent noise, which has a Poisson distribution. Conventional methods usually estimate single or
Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological anal
Positron-annihilation-induced Auger electron spectroscopy (PAES) uses a beam of low-energy positrons to excite Auger transitions via annihilation of core electrons. This mechanism imbues PAES with a high degree of surface specificity and the ability to eliminate the large collisionally induced secondary-electron background typically present in conventional Auger spectra. Here, we describe a high-resolution PAES system with an energy resolution ΔE/E=2.5%, approximately five times better than prev
Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area
The electrocardiogram (ECG) has been known to be affected by demographic and anthropometric factors. This study aimed to develop deep learning models to predict the subject's age, sex, ABO blood type, and body mass index (BMI) based on ECGs. This retrospective study included individuals aged 18 years or older who visited a tertiary referral center with ECGs acquired from October 2010 to February 2020. Using convolutional neural networks (CNNs) with three convolutional layers, five kernel sizes,
Point defects often appear in two-dimensional (2D) materials and are mostly correlated with physical phenomena. The direct visualisation of point defects, followed by statistical inspection, is the most promising way to harness structure-modulated 2D materials. Here, we introduce a deep learning-based platform to identify the point defects in 2H-MoTe<sub>2</sub>: synergy of unit cell detection and defect classification. These processes demonstrate that segmenting the detected hexagonal cell into