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Sub-diffraction error mapping for localisation microscopy images

Author

Listed:
  • Richard J. Marsh

    (King’s College London)

  • Ishan Costello

    (King’s College London)

  • Mark-Alexander Gorey

    (King’s College London)

  • Donghan Ma

    (Purdue University)

  • Fang Huang

    (Purdue University)

  • Mathias Gautel

    (King’s College London)

  • Maddy Parsons

    (King’s College London)

  • Susan Cox

    (King’s College London)

Abstract

Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.

Suggested Citation

  • Richard J. Marsh & Ishan Costello & Mark-Alexander Gorey & Donghan Ma & Fang Huang & Mathias Gautel & Maddy Parsons & Susan Cox, 2021. "Sub-diffraction error mapping for localisation microscopy images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25812-z
    DOI: 10.1038/s41467-021-25812-z
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    Cited by:

    1. Rong Chen & Xiao Tang & Yuxuan Zhao & Zeyu Shen & Meng Zhang & Yusheng Shen & Tiantian Li & Casper Ho Yin Chung & Lijuan Zhang & Ji Wang & Binbin Cui & Peng Fei & Yusong Guo & Shengwang Du & Shuhuai Y, 2023. "Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Kaarjel K. Narayanasamy & Johanna V. Rahm & Siddharth Tourani & Mike Heilemann, 2022. "Fast DNA-PAINT imaging using a deep neural network," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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