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Lung cancer diagnosis with quantitative DIC microscopy and a deep convolutional neural network
Longfei Zheng1, Kangyuan Yu1, Shuangshuang Cai1
1Institute of Lasers and Biomedical Photonics, School of Biomedical Engineering, Wenzhou, Wenzhou Medical University, Wenzhou 325035, China.
Biomedical Optics Express
|June 1, 2019
Summary
Quantitative phase microscopy and deep learning accurately diagnose lung squamous cell carcinoma. This label-free approach shows promise for rapid, in situ cancer detection.
Area of Science:
- Biomedical Optics
- Cancer Diagnostics
- Computational Pathology
Background:
- Lung squamous cell carcinoma (LSCC) diagnosis relies on traditional methods.
- Quantitative phase microscopy offers label-free imaging of unstained tissues.
- Deep learning shows potential in medical image analysis.
Purpose of the Study:
- To develop a label-free method for LSCC diagnosis using quantitative TI-DIC microscopy and deep learning.
- To evaluate the diagnostic performance of optical property maps derived from DIC images.
- To compare the efficacy of a deep convolutional neural network (DCNN) classifier using DIC images versus optical property maps.
Main Methods:
- Quantitative Transport of Intensity Equation (TIE) microscopy to retrieve 2-D phase maps from DIC images of unstained LSCC tissue.
- Computation of spatially resolved optical properties (scattering coefficient, reduced scattering coefficient, anisotropy factor) from phase maps using the scattering-phase theorem.
- Development and application of a DCNN classifier to differentiate between cancerous and non-cancerous tissue using DIC images and derived optical property maps.
Main Results:
- Optical properties, specifically scattering and reduced scattering coefficients, increased, while the anisotropy factor decreased in cancerous tissue.
- The DCNN classifier achieved significantly higher accuracy when using the optical property maps compared to using raw DIC images.
- The proposed label-free quantitative phase microscopy and DCNN approach demonstrated strong diagnostic performance for LSCC.
Conclusions:
- Label-free quantitative phase microscopy combined with DCNN analysis is a powerful tool for LSCC diagnosis.
- Optical property maps derived from DIC images provide superior features for DCNN-based cancer classification compared to direct DIC imaging.
- This integrated approach holds promise for rapid, in situ, and accurate cancer diagnosis, potentially reducing the need for tissue staining.
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