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WindSTORM: Robust online image processing for high-throughput nanoscopy.

Hongqiang MaJianquan XuYang Liu
Published in: Science advances (2019)
High-throughput nanoscopy becomes increasingly important for unraveling complex biological processes from a large heterogeneous cell population at a nanoscale resolution. High-density emitter localization combined with a large field of view and fast imaging frame rate is commonly used to achieve a high imaging throughput, but the image processing speed and the presence of heterogeneous background in the dense emitter scenario remain a bottleneck. Here, we present a simple non-iterative approach, referred to as WindSTORM, to achieve high-speed high-density emitter localization with robust performance for various image characteristics. We demonstrate that WindSTORM improves the computation speed by two orders of magnitude on CPU and three orders of magnitude upon GPU acceleration to realize online image processing, without compromising localization accuracy. Further, WindSTORM is highly robust to maximize the localization accuracy and minimize the image artifacts in the presence of nonuniform background. WindSTORM paves the way for next generation high-throughput nanoscopy.
Keyphrases
  • high density
  • high throughput
  • deep learning
  • high speed
  • single cell
  • high resolution
  • atomic force microscopy
  • social media
  • healthcare
  • magnetic resonance imaging
  • single molecule
  • machine learning
  • cell therapy