Fluorescence Lifetime Endomicroscopic Image-based ex-vivo Human Lung
Cancer Differentiation Using Machine Learning
Abstract
Over 20,000 fluorescence lifetime images from 10 patients were
collected using a fibre-based custom fluorescence lifetime
imaging endomicroscopy (FLIM) system. During the data
collection, various measuring conditions were applied, including
exposure time, optical wavelength, and lifetime extraction approaches to
obtain diverse results rich in spatial and spectral resolution. The data
for further processing was chosen with exposure time of 6 and 20 ns,
excitation bands of 490-570 and 594-764 nm, and RLD. In addition, there
are some images with sizes different than 128x128. In order to avoid any
artificial errors on the lifetime images during the processing, only the
lifetime images with 128x128 resolution were selected. After the
selection, there were 10,155 and 11,363 frames of cancer and normal
tissues respectively, and each frame contained one intensity and one
corresponding lifetime image.