|
|
高光谱显微成像技术及其在病理学检测中的应用
|
Abstract:
在临床实践中，病理学检测通常需要化学染色等多个繁杂步骤且成本昂贵，在术中病理检测等应用中存在极大的局限性。利用高光谱显微成像技术，不仅可对病理组织切片进行无损和快速成像，还可采集到病理组织切片丰富的光谱信息，在对高光谱显微图像进一步数据处理后，可获取关于病理组织切片的生理、生化信息，进而实现快速、准确的病理诊断。高光谱显微成像技术具有图谱合一的特点，避免了化学染色法对组织样本的伤害，不影响标本进行其它检测。本文主要介绍了高光谱显微成像技术及其发展趋势，其中着重阐述了一种新型的可编程高光谱显微成像技术及其所实现的光学染色功能，随后重点介绍了高光谱显微成像技术在病理学检测中的应用现状。
In clinical practice, pathological examination usually requires many complicated steps, such as chemical staining, which is expensive and has great limitations in the application of intraoperative pathological examination. By using hyperspectral microscopic imaging technology, not only can pathological tissue sections be imaged nondestructively and quickly, but also rich spectral information of pathological tissue sections can be collected. After further data processing of hyperspectral microscopic images, physiological and biochemical information about pathological tissue sections can be obtained, and then rapid and accurate pathological diagnosis can be realized. Hyperspectral microscopic imaging technology has the characteristics of atlas integration, which avoids the damage of chemical staining to tissue samples and does not affect other detection of samples. This paper mainly introduces the hyperspectral microscopic imaging technology and its development trend, in which a new type of programmable hyperspectral microscopic imaging technology and its optical staining function are emphasized. Then, the application status of hyperspectral microscopic imaging technology in pathological examination is emphatically introduced.
| [1] | 宋其均. 关于病理检查, 您想知道的可能都在这[J]. 医师在线, 2020(10): 5. |
| [2] | 王玉清. 组织固定的探讨[J]. 辽宁医学院学报, 2007, 28(4): 79-80. |
| [3] | 周闽湘, 袁章军. 浅析高中生物学教材中有关染色剂的几个问题[J]. 生物学通报, 2008, 43(1): 51-52. |
| [4] | 李伟, 吕蒙, 陈天虹, 楚照耀, 陶然. 高光谱图像在生物医学中的应用[J]. 中国图象图形学报, 2021, 26(8): 1764-1785. |
| [5] | 苏康. 基于高光谱显微成像的细胞检测研究与应用[D]: [硕士学位论文]. 广州: 暨南大学, 2017. |
| [6] | Schultz, R., Nielsen, T., Zavaleta, J., Ruch, R., Wyatt, R. and Garner, H.R. (2001) Hyperspectral Imaging: A Novel Approach for Microscopic Analysis. Cytometry, 43, 239-247.
https://doi.org/10.1002/1097-0320(20010401)43:4<239::AID-CYTO1056>3.0.CO;2-Z |
| [7] | Roblyer, D., Kurachi, C., Gillenwater, A. and Richards-Kortum, R. (2009) In Vivo Fluorescence Hyperspectral Imaging of Oral Neoplasia. Proceedings of SPIE, 7169, Article ID: 71690J. https://doi.org/10.1117/12.807226 |
| [8] | Studer, V., Bobin, J., Chahid, M., Mousavi, H.S., Candes, E. and Dahan, M. (2012) Compressive Fluorescence Microscopy for Biological and Hyperspectral Imaging. Proceedings of the National Academy of Sciences of the United States of Americ, 109, E1679-E1687. https://doi.org/10.1073/pnas.1119511109 |
| [9] | Pegoraro, A., Slepkov, A., Ridsdale, A., Moffatt, D.J. and Stolow, A. (2014) Hyperspectral Multimodal CARS Microscopy in the Fingerprint region. Journal of Biophotonics, 7, 49-58. https://doi.org/10.1002/jbio.201200171 |
| [10] | Gao, L., Kester, R.T., Hagen, N. and Tkaczyk, T.S. (2010) Snapshot Image Mapping Spectrometer (IMS) with High Sampling Density for Hyperspectral Microscopy. Optics Express, 18, 14330-14344.
https://doi.org/10.1364/OE.18.014330 |
| [11] | Cui, Q., Park, J., Smith, R.T. and Gao, L. (2020) Snapshot Hyperspectral Light Field Imaging Using Image Mapping Spectrometry. Optics Letters, 45, 772-775. https://doi.org/10.1364/OL.382088 |
| [12] | 王月旗. 基于DMD的编码孔径光谱成像光学系统设计[D]: [硕士学位论文]. 长春: 长春理工大学, 2020.
https://doi.org/10.26977/d.cnki.gccgc.2020.000101 |
| [13] | Rivenson, Y., Wang, H., Wei, Z., de Haan, K., Zhang, Y., Wu, Y., Günayd？n, H., et al. (2019) Virtual Histological Staining of Unlabelled Tissue-Autofluorescence Images via Deep Learning. Nature Biomedical. Engineering, 3, 466- 477. https://doi.org/10.1038/s41551-019-0362-y |
| [14] | 李京蓓. 基于深度学习的图像着色技术研究[D]: [硕士学位论文]. 长沙: 国防科技大学, 2019.
https://doi.org/10.27052/d.cnki.gzjgu.2019.000470 |
| [15] | 贾曦. 基于深度学习的HEp-2细胞荧光染色图像分析[D]: [硕士学位论文]. 深圳: 深圳大学, 2019. |
| [16] | Lu, J., Ren, Y., Xu, W., Cui, X., Xie, L., Chen, S., Guo, J. and Yao, Y. (2019) A Programmable Optical Filter with Arbitrary Transmittance for Fast Spectroscopic Imaging and Spectral Data Post-Processing. IEEE Access, 7, 119294- 119308. https://doi.org/10.1109/ACCESS.2019.2937095 |
| [17] | Zhang, F., Zhang, Z., Wei, L., Xu, W., Cui, X. and Chen, S. (2020) Coding Convolutional Neural Networks as Spectral Transmittance for Intelligent Hyperspectral Remote Sensing in a Snapshot. IEEE Geoscience and Remote Sensing Letters, 18, 1635-1639. https://doi.org/10.1109/LGRS.2020.3005982 |
| [18] | 陈瑶. 基于高光谱显微系统的癌细胞鉴别[D]: [硕士学位论文]. 广州暨南大学, 2020.
https://doi.org/10.27167/d.cnki.gjinu.2020.001050 |
| [19] | Akbari, H., Halig, L., Schuster, D.M., Fei, B., Osunkoya, A., Master, V., et al. (2012) Hyperspectral Imaging and Quantitative Analysis for Prostate Cancer Detection. Journal of Biomedical Optics, 17, Article ID: 076005.
https://doi.org/10.1117/1.JBO.17.7.076005 |
| [20] | Halicek, M., Little, J.V., Wang, X., Patel, M., Griffith, C.C., Chen, A.Y., et al. (2018) Tumor Margin Classification of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, Houston, 12-15 February 2018, Article ID: 1057605.
https://doi.org/10.1117/12.2293167 |
| [21] | Dicker, D.T., Lrner, J., Van Belle, P., Guerry, D., Herlyn, M., Elder, D.E. and El-Deiry, W.S. (2006) Differentiation of Normal Skin and Melanoma Using High Resolution Hyperspectral Imaging. Cancer Biology & Therapy, 5, 1033-1038. |
| [22] | Rlpanasyuk, G. (2005) Early Changes in the Skin Microcirculation and Muscle Metabolism of the Diabetic Foot. Digest of the World Core Medical Journals, 366, 1711-1717. https://doi.org/10.1016/S0140-6736(05)67696-9 |
| [23] | Khaodhiar, L., Dinh, T., Schomacker, K.T., Panasyuk, S.V., Freeman, J.E., Lew, R., et al. (2007) The Use of Medical Hyperspectral Technology to Evaluate Microcirculatory Chaves in Diabetic Foot Ulcers and to Predict Clinical Outcomes. Diabetes Care, 30 903-910. https://doi.org/10.2337/dc06-2209 |
| [24] | Dmitry, Y., Aksone, N. and Laurent, P. (2010) Hyperspectral Imaging in Diabetic Foot Wound Care. Journal of Diabetes Science and Technology, 4, 1099-1113. https://doi.org/10.1177/193229681000400508 |
| [25] | Seroul, P., Hbert, M. and Jomier, M. (2014) Hyperspectral Imaging System for in-Vivo Quantication of Skin Pigments. IFSCC, Paris, 27-30 October 2014, 123-132. |
| [26] | Akbari, H. and Kosugi, Y. (2011) Cancer Detection Using Infrared Hyperspectral Imaging. Cancer Science, 102, 852-857. https://doi.org/10.1111/j.1349-7006.2011.01849.x |
| [27] | Zhu, S., Su, K., Liu, Y., Yin, H., Li, Z., Huang, F., et al. (2015) Identification of Cancerous Gastric Cells Based on Common Features Extracted from Hyperspectral Microscopic Images. Biomedical Optics Express, 6, 1135-1145.
https://doi.org/10.1364/BOE.6.001135 |
| [28] | Kiyotoki, S., Nishikawa, J., Okamoto, T., Hamabe, K., Saito, M., Goto, A., et al. (2013) New Method for Detection of Gastric Cancer by Hyperspectral Imaging: A Pilot Study. Journal of Biomedical Optics, 18, Article ID: 026010.
https://doi.org/10.1117/1.JBO.18.2.026010 |
| [29] | Goto, A., Nishikawa, J., Kiyotoki, S., Nakamura, M., Nishimura, J., Okamoto, T., et al. (2015) Use of Hyperspectral Imaging Technology to Develop a Diagnostic Support System for Gastric Cancer. Journal of biomedical optics, 20, Article ID: 016017. https://doi.org/10.1117/1.JBO.20.1.016017 |
