The Centre has a long history of developing new techniques for medical imaging (particularly in magnetic resonance imaging), transforming them from a primarily diagnostic modality into an interventional and therapeutic platform. This is facilitated by the Centre's strong engineering background in practical imaging and image analysis platform development, as well as advances in minimal access and robotic assisted surgery. Hamlyn has a strong tradition in pursuing basic sciences and theoretical research, with a clear focus on clinical translation.

In response to the current paradigm shift and clinical demand in bringing cellular and molecular imaging modalities to an in vivo – in situ setting during surgical intervention, our recent research has also been focussed on novel biophotonics platforms that can be used for real-time tissue characterisation, functional assessment, and intraoperative guidance during minimally invasive surgery. This includes, for example, SMART confocal laser endomicroscopy, time-resolved fluorescence spectroscopy and flexible FLIM catheters.


Citation

BibTex format

@inproceedings{Ye:10.1007/978-3-319-10470-6_40,
author = {Ye, M and Johns, E and Giannarou, S and Yang, G-Z},
doi = {10.1007/978-3-319-10470-6_40},
pages = {316--323},
publisher = {Springer International Publishing},
title = {Online Scene Association for Endoscopic Navigation},
url = {http://dx.doi.org/10.1007/978-3-319-10470-6_40},
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Endoscopic surveillance is a widely used method for moni-toring abnormal changes in the gastrointestinal tract such as Barrett'sesophagus. Direct visual assessment, however, is both time consumingand error prone, as it involves manual labelling of abnormalities on alarge set of images. To assist surveillance, this paper proposes an onlinescene association scheme to summarise an endoscopic video into scenes,on-the-y. This provides scene clustering based on visual contents, andalso facilitates topological localisation during navigation. The proposedmethod is based on tracking and detection of visual landmarks on thetissue surface. A generative model is proposed for online learning of pair-wise geometrical relationships between landmarks. This enables robustdetection of landmarks and scene association under tissue deformation.Detailed experimental comparison and validation have been conductedon in vivo endoscopic videos to demonstrate the practical value of ourapproach.
AU - Ye,M
AU - Johns,E
AU - Giannarou,S
AU - Yang,G-Z
DO - 10.1007/978-3-319-10470-6_40
EP - 323
PB - Springer International Publishing
SN - 0302-9743
SP - 316
TI - Online Scene Association for Endoscopic Navigation
UR - http://dx.doi.org/10.1007/978-3-319-10470-6_40
UR - http://hdl.handle.net/10044/1/27120
ER -