Label-free blood analysis utilizing contrast-enhanced defocusing imaging with machine vision

D. Chen; N. Li; X. Liu; S. Zeng; X. Lv; L. Chen; Y. Xiao; Q. Hu

doi:10.1117/12.2682246

18 July 2023 Label-free blood analysis utilizing contrast-enhanced defocusing imaging with machine vision

D. Chen, N. Li, X. Liu, S. Zeng, X. Lv, L. Chen, Y. Xiao, Q. Hu

Author Affiliations +

Proceedings Volume 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023); 1274505 (2023) https://doi.org/10.1117/12.2682246
Event: Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 2023, Haikou, China

Abstract

Blood analysis, through the complete blood count, remains the most fundamental medical test for diagnosing broad diseases. Even so, it is still limited to central laboratories with sophisticated facilities and skilled professionals. Here, we propose a simple, machine-vision-aided, label-free blood analysis technique via a regular microscope utilizing contrast-enhance defocus imaging, i.e., defocusing imaging under 415 nm, small aperture illumination. We have shown that this technique can simultaneously obtain leucocytes’ optical phase and erythrocytes’ spectrophotometric information, making it feasible to realize five-part leucocyte differential and hemoglobin quantification with machine vision. The reliability was verified by comparing the quantified results with clinical reference results, which indicates significant linear correlations (significance levels ⪅ 0.0001 and Pearson coefficients ⪆ 0.90). We also show that the virtual staining of the label-free blood cell images can be performed with a generative adversarial network to mimic conventional Wright-Giemsa images, facilitating this technique’s medical translation. This study reports a simple, easy-to-use, quick, reliable blood analysis technique that may lead to a reformation in the blood analysis field. We emphasize this technique’s great potential for early screening of various diseases, including anemia, leukemia, and neglected tropical diseases, especially in resource-limited settings.

Citation Download Citation

D. Chen, N. Li, X. Liu, S. Zeng, X. Lv, L. Chen, Y. Xiao, and Q. Hu "Label-free blood analysis utilizing contrast-enhanced defocusing imaging with machine vision", Proc. SPIE 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 1274505 (18 July 2023); https://doi.org/10.1117/12.2682246

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KEYWORDS

Blood

Machine vision

Biomedical engineering

Optical medical imaging

Point-of-care devices

Thin film diagnostics

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