Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches.

Journal: International journal of computer assisted radiology and surgery
Published Date: May 15, 2020

Abstract

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation. It was shown that convolutional neural networks (CNNs) could improve pCLE image quality. Yet classical CNNs may be suboptimal in regard to irregular data.

Authors

  • Agnieszka Barbara Szczotka
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK. agnieszka.szczotka.15@ucl.ac.uk.
  • Dzhoshkun Ismail Shakir
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Daniele Ravi
  • Matthew J Clarkson
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Stephen P Pereira
    UCL Institute for Liver and Digestive Health, University College London, London, UK.
  • Tom Vercauteren
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.

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