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Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

Ravì, D; Szczotka, AB; Shakir, DI; Pereira, SP; Vercauteren, T; (2018) Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction. International Journal of Computer Assisted Radiology and Surgery 10.1007/s11548-018-1764-0. (In press). Green open access

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Abstract

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. METHODS: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). RESULTS: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. CONCLUSION: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images.

Type: Article
Title: Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Location: Germany
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11548-018-1764-0
Publisher version: https://doi.org/10.1007/s11548-018-1764-0
Language: English
Additional information: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Example-based super-resolution, Deep learning, Probe-based confocal laser endomicroscopy, Mosaicking
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inst for Liver and Digestive Hlth
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: http://discovery.ucl.ac.uk/id/eprint/10047355
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