Endoscopic depth measurement and super-spectral-resolution imaging
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Intra-operative measurements of tissue shape and multi/hyperspectral information have the potential to provide surgical guidance and decision making support. We report an optical probe based system to combine sparse hyperspectral measurements and spectrally-encoded structured lighting (SL) for surface measurements. The system provides informative signals for navigation with a surgical interface. By rapidly switching between SL and white light (WL) modes, SL information is combined with structure-from-motion (SfM) from white light images, based on SURF feature detection and Lucas-Kanade (LK) optical flow to provide quasi-dense surface shape reconstruction with known scale in real-time. Furthermore, “super-spectral-resolution” was realized, whereby the RGB images and sparse hyperspectral data were integrated to recover dense pixel-level hyperspectral stacks, by using convolutional neural networks to upscale the wavelength dimension. Validation and demonstration of this system is reported on ex vivo/in vivo animal/human experiments.
Date Issued
2017-09-04
Date Acceptance
2017-09-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10434, pp.39-47
ISBN
9783319661841
ISSN
0302-9743
Publisher
Springer Nature Switzerland AG
Start Page
39
End Page
47
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10434
Copyright Statement
© 2017 Springer-Verlag. The final publication is available at Springer via https://dx.doi.org/10.1007/978-3-319-66185-8_5
Sponsor
Commission of the European Communities
Commission of the European Communities
Grant Number
242991
PIEF-GA-2012-332226
Source
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
Subjects
cs.CV
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2017-09-11
Finish Date
2017-09-13
Coverage Spatial
Quebec City, QC, Canada
Date Publish Online
2017-09-04