Machine learningMicroscopySuper-resolution microscopyThe speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many th
Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of fra...
Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of fra...
describes how deep learning is used to improve super-resolution microscopy, and explains why the journal plays a part in improving laboratory techniques and methods.
恭喜中科院生物物理所李栋研究员,清华大学戴琼海院士,霍华德休斯医学研究所Janelia研究园区Jennifer Lippincott-Schwartz院士,论文题为:Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processesP2:Nature Immunology封面上刊!上海交通大学李华兵研究员,重庆国际免疫研究院...
Discussion of the role of artificial intelligence in microscopy image analysis, including use of Nikon’s NIS.ai deep learning-based software analysis modules.
Deep Learning in Microscopy Products Related Literature Discussion Related Solutions Glossary Request InformationMicroscope Products Confocal and Multiphoton Microscopes Super-Resolution Microscopes Inverted Microscopes Upright Microscopes Cell Screening High Content Imaging Slide Scanning Polarizing Microscopes Stereo Mic...
The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artifi...
D15 Reconstruct compressed sensing MRI to dealiased image DAGANf: conditional GANg stabilized by refinement learning, with the content loss combined adversarial loss incorporating frequency domain data D16 Reconstruct sparse localization microscopy to superresolution image Artificial Neural Network Accelerated–...
The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. ...