Leveraging uncertainty quantification in adaptive multiphoton microscopy acquisition

Cassandra Tong Ye; Jiashu Han; Kunzan Liu; Anastasios Angelopoulos; Linda Griffith; Kristina Monakhova; Sixian You

doi:10.1117/12.2692749

13 March 2024 Leveraging uncertainty quantification in adaptive multiphoton microscopy acquisition

Cassandra Tong Ye, Jiashu Han, Kunzan Liu, Anastasios Angelopoulos, Linda Griffith, Kristina Monakhova, Sixian You

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 1285707 (2024) https://doi.org/10.1117/12.2692749
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Multiphoton microscopy (MPM) provides high-resolution imaging of deep tissue structures while allowing for the visualization of non-labeled biological samples. However, photon generation efficiency of intrinsic biomarkers is low and this, coupled with inherent detection inaccuracies in the photoelectric sensors, leads to an introduction of noise in acquired images. Higher dwelling times can reduce noise but increase the likelihood of photobleaching. To combat this, deep learning methods are being increasingly employed to denoise MPM images, allowing for a more efficient and less invasive process. However, machine learning models can hallucinate information, which is unacceptable for critical scientific microscopy applications. Uncertainty quantification, which has been demonstrated for image-to-image regression tasks, can provide confidence bounds for machine learning-based image reconstruction tasks, adding confidence to predictions. In this work, we discuss incorporating uncertainty quantification into an optimized denoising model to guide adaptive multiphoton microscopy image acquisition. We demonstrate that our method is capable of maintaining fine features in the denoised image, while outperforming other denoising methods by adaptively selecting to reimage the most uncertain pixels in a human endometrium tissue sample.

Conference Presentation

Citation Download Citation

Cassandra Tong Ye, Jiashu Han, Kunzan Liu, Anastasios Angelopoulos, Linda Griffith, Kristina Monakhova, and Sixian You "Leveraging uncertainty quantification in adaptive multiphoton microscopy acquisition", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 1285707 (13 March 2024); https://doi.org/10.1117/12.2692749

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