Identification of immune cell types based on label-free multiphoton imaging and deep learning

Lucas A. Kreiss; Amey Chaware; Kanghyun Kim; Kyung Chul Lee; Shiqi Xu; Oana-Maria Thoma; Sarah Lemire; Oliver Friedrich; Sebastian Schürmann; Maximilian Waldner; Roarke W. Horstmeyer

doi:10.1117/12.3001883

19 March 2024 Identification of immune cell types based on label-free multiphoton imaging and deep learning

Lucas A. Kreiss, Amey Chaware, Kanghyun Kim, Kyung Chul Lee, Shiqi Xu, Oana-Maria Thoma, Sarah Lemire, Oliver Friedrich, Sebastian Schürmann, Maximilian Waldner, Roarke W. Horstmeyer

Author Affiliations +

Proceedings Volume PC12822, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2024; PC1282209 (2024) https://doi.org/10.1117/12.3001883
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Conventional imaging techniques target this problem by using specific antibody markers. Although such markers allow decent specificity, they are often limited in the field of application, especially for in vivo use, which limits the potential for clinical translations. In contrast to that, label-free optical technologies, like multiphoton microscopy (MPM), can generate highly resolved 3D images from unstained samples, by exploiting natural optical contrast. Label-free MPM can show epithelial damage and immune infiltration in unstained colon samples. Here, we imaged a mixture of T cells and neutrophils with label-free MPM. In order to obtain ground-truth images, we simultaneously recorded images of a Cd4+ specific fluorescent marker for T cells. A deep neural network was then trained for the segmentation of T cells and neutrophils based on such label-free MPM images. Upon training, this model can then be used to detect both cell types without relying on specific fluorescent markers, that were used to obtain ground truth. In the future, the augmentation of label-free MPM by such computational specificity could have great potential for in vivo endomicroscopy.

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Lucas A. Kreiss, Amey Chaware, Kanghyun Kim, Kyung Chul Lee, Shiqi Xu, Oana-Maria Thoma, Sarah Lemire, Oliver Friedrich, Sebastian Schürmann, Maximilian Waldner, and Roarke W. Horstmeyer "Identification of immune cell types based on label-free multiphoton imaging and deep learning", Proc. SPIE PC12822, Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2024, PC1282209 (19 March 2024); https://doi.org/10.1117/12.3001883

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KEYWORDS

Deep learning

Tissues

In vivo imaging

Biological samples

Biopsy

Diseases and disorders

Education and training

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