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5 March 2021 Generalizable physics-embedded deep learning for recovering complex 3D biology with intensity diffraction tomography
Alex C. Matlock, Lei Tian
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Proceedings Volume 11653, Quantitative Phase Imaging VII; 116530W (2021) https://doi.org/10.1117/12.2576788
Event: SPIE BiOS, 2021, Online Only
Abstract
We present a physics-embedded deep learning architecture for improving the intensity diffraction tomography’s (IDT) 3D quantitate phase recovery of complex biological samples. Our approach utilizes both convolutional neural networks and IDT’s model-based volumetric reconstructions to provide accurate, physically-constrained predictions of multiple-scattering object volumes. We train and validate this learned IDT pipeline on simulated natural image datasets and show it generalizes well to recovering unseen complex biological specimens from experimental data.
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Alex C. Matlock and Lei Tian "Generalizable physics-embedded deep learning for recovering complex 3D biology with intensity diffraction tomography", Proc. SPIE 11653, Quantitative Phase Imaging VII, 116530W (5 March 2021); https://doi.org/10.1117/12.2576788
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KEYWORDS
Biology
Diffraction
Tomography
Multiplexing
Optical microscopes
Optical tomography
Phase imaging
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