제5회 막수 포럼 (The 5th Last Wednesday Forum)
일시: 2013년 10월 2일, 12:00~13:00
장소: 전산학과(E3-1) 4층 오상수 영상강의실
3D Shape Modeling and Analyzing Method for the Lateral Ventricle of Normal Aging People
Presenter: 류호진 (박진아 교수님 연구실)
The Lateral Ventricle is a cavity of the cerebral hemisphere and is filled with CSF. Its global structure can be anatomically divided into three horns; frontal, temporal and occipital. Each horn reflects the atrophy of the surrounded subcortical structures by enlarging its shape and size. Since the CSF regions are able to be extracted more preciously than nearby tissues in the MR image, recently the quantitative measurement of the Lateral Ventricle is regarded as a sensitive biomarker. Therefore, the aim of our study is developing a shape modeling and analysis method, which is able to detect sensitively the morphological changes of the Lateral Ventricle. The MR and binary data that we use in this study are acquired by the Lothian Birth Cohort 1936, which investigates the determinants of differences in cognitive aging. In this presentation we will discuss our ongoing research on modeling and analyzing the shape of the Lateral Ventricle and its challenging problems.
An Algorithm for High-Density Super-Resolution Microscopy
Presenter: 민준홍 (예종철 교수님 연구실)
Super resolution microscopy such as STORM and (F)PALM is now well known method for biological study at the nanometer scale. However, conventional imaging scheme based on sparse activation of photo-switchable fluorescent probes has inherently slow temporal resolution which is a serious limitation when investigating live-cell dynamics. Here, we present an algorithm for high-density super-resolution microscopy which combines a sparsity-promoting formulation with Taylor series approximation of the PSF. Our algorithm is designed to provide unbiased localization on continuous space and high recall rates for high density imaging, and to have orders-of-magnitude faster run time compared to currently available high-density algorithms. We validated our algorithm on both simulated and experimental data, and demonstrated live-cell imaging with temporal resolution of 2.5 s. by recovering fast ER dynamics.