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

Longfei Zheng,Min Xu,Bixin Zeng,Kangyuan Yu,Shuangshuang Cai,Yu Wang

doi:10.1364/boe.10.002446

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 ( ) and the reduced scattering coefficient ( ) 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 , 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 in situ rapid cancer diagnosis.

Highlights

The morbidity and mortality rate of lung cancer is the highest among all cancers, both in term of new cases (2.09 million cases, 11.6% of total) and deaths (1.76 million deaths, 18.4%) among the 18.07 million new cancer cases and 9.55 million cancer deaths occurred in 2018 worldwide [1]
The differential interference contrast (DIC) images of in-focus and out-of-focus of the unstained slides were taken at the corresponding location for each ease, from which the 2-D phase maps and the scattering properties were computed
We have demonstrated a method for the diagnosis of lung squamous cell carcinoma by TI-DIC microscope and deep convolutional neural network

Summary

Introduction

The morbidity and mortality rate of lung cancer is the highest among all cancers, both in term of new cases (2.09 million cases, 11.6% of total) and deaths (1.76 million deaths, 18.4%) among the 18.07 million new cancer cases and 9.55 million cancer deaths occurred in 2018 worldwide [1]. Traditional pathological diagnosis requires time-consuming multi-step tissue preparation and is not suitable for rapid diagnosis. During the past two decades, much efforts have been devoted to developing label-free optical techniques for in situ rapid diagnosis of cancer. Both quantitative phase imaging and tissue native fluorescence have shown great potential [3,4,5,6,7,8,9]. A DCNN classifier has been developed to classify excised squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples based on Hyperspectral Imaging (HSI) [13]. The spatial structural information contained in HSI was, discarded

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Discussion

Conclusion