activity
20182021
most citedThree dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks

18 citations · 19 across the 2 of their papers we have counts for

collaborators

6 papers

eess.IV20211 cited

RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid Detection in Three-Dimensional Fluorescence Microscopy Images

Liming Wu, Shuo Han, Alain Chen +3

Robust and accurate nuclei centroid detection is important for the understanding of biological structures in fluorescence microscopy images. Existing automated nuclei localization…

eess.IV2021

Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)

Varun Mannam, Yide Zhang, Xiaotong Yuan +6

Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups.…

eess.IV2019

Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images

David Joon Ho, Shuo Han, Chichen Fu +3

Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentati…

physics.bio-ph2019

Quantitative Kinetic Models from Intravital Microcopy: A Case Study Using Hepatic Transport

Meysam Tavakoli, Konstantinos Tsekouras, Richard Day +2

The liver performs critical physiological functions, including metabolizing and removing substances, such as toxins and drugs, from the bloodstream. Hepatotoxicity itself is intima…

cs.CV201918 cited

Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks

Soonam Lee, Shuo Han, Paul Salama +2

Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from…

cs.CV2018

Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction

Soonam Lee, Chichen Fu, Paul Salama +2

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological stru…