Localization of Ultra-dense Emitters with Neural Networks

• URL: http://arxiv.org/abs/2305.05542v1
• Date: Sun, 7 May 2023 19:20:42 GMT
• Title: Localization of Ultra-dense Emitters with Neural Networks
• Authors: Armin Abdehkakha and Craig Snoeyink
• Abstract summary: We present a deep convolutional neural network called LUENN which utilizes a unique architecture that rejects the isolated emitter assumption. It can smoothly accommodate emitters that range from completely isolated to co-located. This architecture, alongside an accurate estimator of location uncertainty, extends the range of usable emitter densities by a factor of 6 to over 31 emitters per micrometer-squared with reduced penalty to localization precision and improved temporal resolution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-Molecule Localization Microscopy (SMLM) has expanded our ability to visualize subcellular structures but is limited in its temporal resolution. Increasing emitter density will improve temporal resolution, but current analysis algorithms struggle as emitter images significantly overlap. Here we present a deep convolutional neural network called LUENN which utilizes a unique architecture that rejects the isolated emitter assumption; it can smoothly accommodate emitters that range from completely isolated to co-located. This architecture, alongside an accurate estimator of location uncertainty, extends the range of usable emitter densities by a factor of 6 to over 31 emitters per micrometer-squared with reduced penalty to localization precision and improved temporal resolution. Apart from providing uncertainty estimation, the algorithm improves usability in laboratories by reducing imaging times and easing requirements for successful experiments.

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