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Application of machine learning method in optical molecular imaging: a review

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Abstract

Optical molecular imaging (OMI) is an imaging technology that uses an optical signal, such as near-infrared light, to detect biological tissue in organisms. Because of its specific and sensitive imaging performance, it is applied in both preclinical research and clinical surgery. However, it requires heavy data analysis and a complex mathematical model of tomographic imaging. In recent years, machine learning (ML)-based artificial intelligence has been used in different fields because of its ability to perform powerful data processing. Its analytical capability for processing complex and large data provides a feasible scheme for the requirement of OMI. In this paper, we review ML-based methods applied in different OMI modalities.

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Acknowledgements

This work was supported by Ministry of Science and Technology of China (Grant Nos. 2018YFC0910602, 2017YFA0205200, 2017YFA0700401, 2016YFA0100902, 2016YFC0103702), National Natural Science Foundation of China (Grant Nos. 61901472, 61671449, 81227901, 81527805), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos. XDB32030200, XDB01030200), Chinese Academy of Sciences (Grant Nos. GJJSTD20170004, YJKYYQ20180048, KFJ-STS-ZDTP-059, QYZDJ-SSW-JSC005), Beijing Municipal Science & Technology Commission (Grant Nos. Z161100002616022, Z171100000117023), and General Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2017M620952). The authors would like to acknowledge the instrumental and technical support of Multi-modal biomedical imaging experimental platform, Institute of Automation, Chinese Academy of Sciences.

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  1. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yu An, Hui Meng, Yuan Gao, Tong Tong, Chong Zhang, Kun Wang & Jie Tian

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hui Meng, Yuan Gao, Tong Tong & Chong Zhang

  3. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China

    Jie Tian

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  1. Yu An
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An, Y., Meng, H., Gao, Y. et al. Application of machine learning method in optical molecular imaging: a review. Sci. China Inf. Sci. 63, 111101 (2020). https://doi.org/10.1007/s11432-019-2708-1

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