Lung cancer diagnosis with quantitative DIC microscopy and a deep convolutional neural network

Abstract

We present a study on lung squamous cell carcinoma diagnosis using quantitative TI-DIC microscopy and a deep convolutional neural network (DCNN). The 2-D phase map of unstained tissue sections is first retrieved from through-focus differential interference contrast (DIC) images based on the transport of intensity equation (TIE). The spatially resolved optical properties are then computed from the 2-D phase map via the scattering-phase theorem. The scattering coefficient ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>μ</mml:mi> <mml:mtext>S</mml:mtext></mml:msub> </mml:mrow> </mml:math> ) and the reduced scattering coefficient ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>μ</mml:mi> <mml:mtext>S</mml:mtext> <mml:mo>'</mml:mo></mml:msubsup> </mml:mrow> </mml:math> ) are found to increase whereas the anisotropy factor (g) is found to decrease with cancer. A DCNN classifier is developed afterwards to classify the tissue using either the DIC images or 2-D optical property maps of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>μ</mml:mi> <mml:mtext>S</mml:mtext></mml:msub> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>μ</mml:mi> <mml:mtext>S</mml:mtext> <mml:mo>'</mml:mo></mml:msubsup> </mml:mrow> </mml:math> and g. The DCNN classifier with the optical property maps exhibits high accuracy, significantly outperforming the same DCNN classifier on the DIC images. The label-free quantitative phase microscopy together with deep learning may emerge as a promising approach for <i>in situ</i> rapid cancer diagnosis.