Histological classification of atherosclerotic arteries using high-speed confocal Raman microscopy with machine learning |
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| Authors: | Jingchao Xing Dong-Ryoung Lee Jin Won Kim Hongki Yoo |
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| Institution: | 1. Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea;2. School of Mechanical Engineering, Soongsil University, Seoul, Republic of Korea;3. Multimodal Imagng and Theranostic Laboratory, Cardiovascular Center, Korea University Guro Hospital, Seoul, Republic of Korea;4. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea |
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| Abstract: | Confocal Raman microscopy is a useful tool to observe composition and constitution of label-free samples at high spatial resolution. However, accurate characterization of microstructure of tissue and its application in diagnostic imaging are challenging due to weak Raman scattering signal and complex chemical composition of tissue. We have developed a method to improve imaging speed, diffraction efficiency, and spectral resolution of confocal Raman microscopy. In addition to the novel imaging technique, the machine learning method enables confocal Raman microscopy to visualize accurate histology of tissue sections. Here, we have demonstrated the performance of the proposed method by measuring histological classification of atherosclerotic arteries and compared the histological confocal Raman images with the conventional staining method. Our new confocal Raman microscopy enables us to comprehend the structure and biochemical composition of tissue and diagnose the buildup of atherosclerotic plaques in the arterial wall without labeling. |
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| Keywords: | confocal microscopy histology machine learning microscopic angioscopy Raman spectroscopy |
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