Image computing for fibre-bundle endomicroscopy: A review

Abstract

Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed.

38 Pages, 2 Figures, 6 Tables

References in corpus (8)

• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
• Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization for Intraoperative CLE Images
• Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
• Deconvolution and Restoration of Optical Endomicroscopy Images
• Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks
• Understanding Soft-Tissue Behavior for Application to Microlaparoscopic Surface Scan
• Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma Images

Cited by in corpus (2)

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