Imperial College London
Alternate

Professor Daniel Elson

Faculty of Medicine, Department of Surgery & Cancer

Professor of Surgical Imaging
 
 
 
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Contact

 

+44 (0)20 7594 1700daniel.elson Website CV

 
 
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Location

 

415 Bessemer BuildingBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lin:2018:10.1016/j.media.2018.06.004,
author = {Lin, J and Clancy, NT and Qi, J and Hu, Y and Tatla, T and Stoyanov, D and Maier-Hein, L and Elson, DS},
doi = {10.1016/j.media.2018.06.004},
journal = {Medical Image Analysis},
pages = {162--176},
title = {Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks.},
url = {http://dx.doi.org/10.1016/j.media.2018.06.004},
volume = {48},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Surgical guidance and decision making could be improved with accurate and real-time measurement of intra-operative data including shape and spectral information of the tissue surface. In this work, a dual-modality endoscopic system has been proposed to enable tissue surface shape reconstruction and hyperspectral imaging (HSI). This system centers around a probe comprised of an incoherent fiber bundle, whose fiber arrangement is different at the two ends, and miniature imaging optics. For 3D reconstruction with structured light (SL), a light pattern formed of randomly distributed spots with different colors is projected onto the tissue surface, creating artificial texture. Pattern decoding with a Convolutional Neural Network (CNN) model and a customized feature descriptor enables real-time 3D surface reconstruction at approximately 12 frames per second (FPS). In HSI mode, spatially sparse hyperspectral signals from the tissue surface can be captured with a slit hyperspectral imager in a single snapshot. A CNN based super-resolution model, namely "super-spectral-resolution" network (SSRNet), has also been developed to estimate pixel-level dense hypercubes from the endoscope cameras standard RGB images and the sparse hyperspectral signals, at approximately 2 FPS. The probe, with a 2.1mm diameter, enables the system to be used with endoscope working channels. Furthermore, since data acquisition in both modes can be accomplished in one snapshot, operation of this system in clinical applications is minimally affected by tissue surface movement and deformation. The whole apparatus has been validated on phantoms and tissue (ex vivo and in vivo), while initial measurements on patients during laryngeal surgery show its potential in real-world clinical applications.
AU  - Lin,J
AU  - Clancy,NT
AU  - Qi,J
AU  - Hu,Y
AU  - Tatla,T
AU  - Stoyanov,D
AU  - Maier-Hein,L
AU  - Elson,DS
DO  - 10.1016/j.media.2018.06.004
EP  - 176
PY  - 2018///
SN  - 1361-8415
SP  - 162
TI  - Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks.
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2018.06.004
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29933116
UR  - http://hdl.handle.net/10044/1/61341
VL  - 48
ER  -
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