Fluorescence Lifetime Endomicroscopic Image-based ex-vivo Human Lung Cancer Differentiation Using Machine Learning

Abstract

<div><i>Over 20,000 fluorescence lifetime images from 10 patients were collected using a fibre-based custom </i><i>fluorescence lifetime imaging endomicroscopy (FLIM)</i><i> system. </i>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.<br></div>

A layer-level multi-scale architecture for lung cancer classification with fluorescence lifetime imaging endomicroscopy

“…Considerable effort has been made by the authors to investigate the automatic classification of ex-vivo lung cancer from FLIM images. In [17], we applied four popular ML methods to FLIM images for ex-vivo lung cancer classification, namely K-nearest neighbour, SVM, neural network, and random forest. A significant difference between our approach and the existing ones is that we applied pixel values of lifetime images as features, instead of artificial ones.…”

Review of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis Using Machine Learning

“…Here, the authors used segmentation as the initial step for classification. Also, in reference [40], the authors used a simple thresholding-based segmentation to remove background before training an ML model on the dataset. This work is also discussed in detail in the next section.…”

“…Wang et al [40] aimed to classify healthy and cancerous lung tissue by four different ML methods (K-nearest neighbor (KNN), support vector classifier (SVC), neural network (NN), random forest (RF)). First, almost 20,000 fluorescence image frames (each frame contains one intensity and corresponding lifetime images) with a dimension of 128×128 px were collected from 10 patients (cancerous and non-cancerous).…”

“…This means that correlations between adjacent pixels were lost. To fill this gap, the authors from [40] extended their work, which is mentioned in paper [42], and they used deep learning for classification. The authors collected 70,000 images with a dimension of 128 × 128.…”