Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ.
Marcel MüllerViola MönkemöllerSimon HennigWolfgang HübnerThomas HuserPublished in: Nature communications (2016)
Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses.
Keyphrases
- deep learning
- single molecule
- high resolution
- machine learning
- high throughput
- optical coherence tomography
- primary care
- high speed
- healthcare
- electronic health record
- big data
- artificial intelligence
- quality improvement
- label free
- computed tomography
- magnetic resonance imaging
- mass spectrometry
- energy transfer
- single cell