Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.
A Coherent Anti-Stokes Raman Scattering (CARS) microendoscope probe for early stage label-free prostate cancer diagnosis at single cell resolution is presented. The handheld CARS microendoscope probe includes a customized micro-electromechanical systems (MEMS) scanning mirror as well as miniature optical and mechanical components. In our design, the excitation laser (pump and stokes beams) from the fiber is collimated, reflected by the reflecting mirror, and transmitted via a 2D MEMS scanning mirror and a micro-objective system onto the sample; emission in the epi-direction is returned through the micro-objective lens, MEMS and reflecting mirror, and collimation system, and finally the emission signal is collected by a photomultiplier tube (PMT). The exit pupil diameter of the collimator system is designed to match the diameter of the MEMS mirror and the entrance pupil diameter of the micro-objective system. The back aperture diameter of the micro-objective system is designed according to the largest MEMS scanning angle and the distance between the MEMS mirror and the back aperture. To increase the numerical aperture (NA) of the micro-objective system in order to enhance the signal collection efficiency, the back aperture diameter of the micro-objective system is enlarged with an upfront achromatic wide angle Keplerian telescope beam expander. The integration of a miniaturized micro-optics probe with optical fiber CARS microscopy opens up the possibility of in vivo molecular imaging for cancer diagnosis and surgical intervention.
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