High-resolution virtual biopsy: non-invasive pathology using deep learning and optical coherence tomography

Aidan Van Vleck; Yonatan Winetraub; Jingjing Zhao; Adam de la Zerda

doi:10.1117/12.3004227

13 March 2024 High-resolution virtual biopsy: non-invasive pathology using deep learning and optical coherence tomography

Aidan Van Vleck, Yonatan Winetraub, Jingjing Zhao, Adam de la Zerda

Author Affiliations +

Proceedings Volume PC12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII; PC128460M (2024) https://doi.org/10.1117/12.3004227
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Virtual biopsy enables non-invasive diagnosis through machine-learning analysis of high-resolution images. But difficulties in generating accurate co-registered training sets and the resolution/field-of-view (FOV) tradeoff have hindered clinical applications. We present a method enabling reliable and accurate co-registration of 3D cellular-resolution OCT of fresh human skin with downstream histology. Orientation data is encoded across the sample by photobleaching a fiduciary grid pattern into fluorescent gel encasing the tissue. These markers persist in histology sections, permitting accurate co-registration to the 3D volume within ~20µm, and enabling cellular-resolution imaging with a cm-level FOV by laterally tiling OCT volumes, crucial steps toward in-vivo high-resolution Virtual Biopsy.

Conference Presentation

Citation Download Citation

Aidan Van Vleck, Yonatan Winetraub, Jingjing Zhao, and Adam de la Zerda "High-resolution virtual biopsy: non-invasive pathology using deep learning and optical coherence tomography", Proc. SPIE PC12846, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XXII, PC128460M (13 March 2024); https://doi.org/10.1117/12.3004227

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KEYWORDS

Biopsy

Tissues

Optical coherence tomography

3D scanning

Image quality

Pathology

Education and training

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