Denoising of depth-resolved, label-free, two-photon images using deep-learning-based algorithms

Nilay Vora; Christopher Polleys; Filippos Sakellariou; Hong-Thao N. Thieu; Elizabeth M. Genega; Irene Georgakoudi

doi:10.1117/12.2650856

16 March 2023 Denoising of depth-resolved, label-free, two-photon images using deep-learning-based algorithms

Nilay Vora, Christopher Polleys, Filippos Sakellariou, Hong-Thao N. Thieu, Elizabeth M. Genega, Irene Georgakoudi

Proceedings Volume 12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023; 1239107 (2023) https://doi.org/10.1117/12.2650856
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

The potential to differentiate between diseased and healthy tissue has been demonstrated through the extraction of morphological and functional metrics from label-free, two-photon images. Acquiring such images as fast as possible without compromising their diagnostic and functional content is critical for clinical translation of two-photon imaging. Computational restoration methods have demonstrated impressive recovery of image quality and important biological information. However, access to large clinical datasets has hampered advancement of denoising algorithms. Here, we seek to demonstrate the application of denoising algorithms on depth-resolved two-photon excited fluorescence (TPEF) images with specific focus on recovery of functional metabolic metrics. Datasets were generated through the collection of images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins from freshly excised rat cheek epithelium. Image datasets were patched across depth, generating 1012, 256-by-256 patches. A well-known U-net architecture was trained on 6628 low-signal-to-noise-ratio (SNR) patches from a previously collected large dataset and later retrained on a smaller 620 low-SNR patches dataset before being validated and evaluated on 88 and 304 low-SNR patches, respectively, using a structural similarity index measure (SSIM) loss function. We demonstrate models trained on larger datasets of human cervical tissue could be used to successfully restore metabolic metrics with an improvement in image quality when applied to rat cheek epithelium images. These results motivate further exploration of weight transfer for denoising of small clinical two-photon microscopy datasets.

Conference Presentation

Citation Download Citation

Nilay Vora, Christopher Polleys, Filippos Sakellariou, Hong-Thao N. Thieu, Elizabeth M. Genega, and Irene Georgakoudi "Denoising of depth-resolved, label-free, two-photon images using deep-learning-based algorithms", Proc. SPIE 12391, Label-free Biomedical Imaging and Sensing (LBIS) 2023, 1239107 (16 March 2023); https://doi.org/10.1117/12.2650856

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