@article {69719, title = {Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopyAbstract}, journal = {Nature Communications}, volume = {16}, year = {2025}, month = {Jan-12-2025}, abstract = {

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained {\textquoteleft}de-aberration{\textquoteright} networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in\ C. elegans\ embryos.

}, doi = {10.1038/s41467-024-55267-x}, url = {https://www.nature.com/articles/s41467-024-55267-xhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x}, author = {Guo, Min and Wu, Yicong and Hobson, Chad M. and Su, Yijun and Qian, Shuhao and Krueger, Eric and Christensen, Ryan and Kroeschell, Grant and Bui, Johnny and Chaw, Matthew and Zhang, Lixia and Liu, Jiamin and Hou, Xuekai and Han, Xiaofei and Lu, Zhiye and Ma, Xuefei and Zhovmer, Alexander and Combs, Christian and Moyle, Mark and Yemini, Eviatar and Liu, Huafeng and Liu, Zhiyi and Benedetto, Alexandre and La Riviere, Patrick and Col{\'o}n-Ramos, Daniel and Shroff, Hari} } @article {69655, title = {Rapid Whole-Organ Characterization via Quantitative Light-Sheet Microscopy}, journal = {Laser \& Photonics Reviews}, year = {2024}, month = {2024 Oct 22}, pages = {2401177}, abstract = {

Whole-organ imaging and characterization at a submicron level provide abundant information on development and diseases while remaining a big challenge, especially in the context of time load. Herein, a quantitative light-sheet microscopy platform that enabled highly time-efficient assessments of fibrous structures within the intact cleared tissue is developed. Dual-view inverted selective plane illumination microscopy (diSPIM), followed by improved registration and deconvolution, led to submicron isotropic imaging of mouse upper genital tract with one hundred-fold speed-ups than previous efforts. Further, optical metrics quantifying 3D local density and structural complexity of targets based on parallel and vectorized convolution in both spatial and frequency domains are developed. Collectively, ≈400{\textendash}2000 fold increases in time efficiency counting for imaging, postprocessing, and quantitative characterization compared to the traditional method is gained. Using this platform, automatic identification of medulla and cortex within the mouse ovary at over 90\% overlap with manual selection by anatomy experts is achieved. Additionally, heterogeneous distributions of immune cells in the mouse ovary and fallopian tube, offering a unique perspective for understanding the immune microenvironment are discovered. This work paves the way for future whole-organ study, and exhibits potential with promise for offering mechanistic insights into physiological and pathological alterations of biological tissues.

}, keywords = {immune microenvironment, light-sheet microscopy, quantitative characterization, whole-organ imaging}, doi = {10.1002/lpor.202401177}, url = {https://doi.org/10.1002/lpor.202401177}, author = {Chen, Lingmei and Su, Yijun and Qian, Shuhao and Zhou, Lingxi and Han, Tao and Wang, Chuncheng and Jiang, Rushan and Ding, Zhihua and Guo, Min and Liu, Zhiyi} }