@article {69558,
	title = {Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation.},
	journal = {Sci Rep},
	volume = {14},
	year = {2024},
	month = {2024 Aug 06},
	pages = {18184},
	abstract = {<p>Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.</p>
},
	issn = {2045-2322},
	doi = {10.1038/s41598-024-68918-2},
	url = {https://doi.org/10.1038/s41598-024-68918-2},
	author = {Rehman, Azaan and Zhovmer, Alexander and Sato, Ryo and Mukouyama, Yoh-Suke and Chen, Jiji and Rissone, Alberto and Puertollano, Rosa and Liu, Jiamin and Vishwasrao, Harshad D and Shroff, Hari and Combs, Christian A and Xue, Hui}
}
@article {68409,
	title = {Practical considerations for quantitative light sheet fluorescence microscopy.},
	journal = {Nature Methods},
	volume = {19},
	year = {2022},
	month = {2022 Dec 01},
	pages = {1538-49},
	abstract = {<p>Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.</p>
},
	issn = {1548-7105},
	doi = {10.1038/s41592-022-01632-x},
	author = {Hobson, Chad M and Guo, Min and Vishwasrao, Harshad D and Wu, Yicong and Shroff, Hari and Chew, Teng-Leong}
}
