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.
CITATION STYLE
Lin, J., Clancy, N. T., Hu, Y., Qi, J., Tatla, T., Stoyanov, D., … Elson, D. S. (2017). Endoscopic depth measurement and super-spectral-resolution imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10434 LNCS, pp. 39–47). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_5
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