TY - JOUR T1 - Spectral organ fingerprints for intraoperative tissue classification with hyperspectral imaging JF - bioRxiv DO - 10.1101/2021.11.24.469943 SP - 2021.11.24.469943 AU - A. Studier-Fischer AU - S. Seidlitz AU - J. Sellner AU - M. Wiesenfarth AU - L. Ayala AU - B. Özdemir AU - J. Odenthal AU - S. Knödler AU - K.F. Kowalewski AU - C.M. Haney AU - I. Camplisson AU - M. Dietrich AU - K. Schmidt AU - G.A. Salg AU - H.G. Kenngott AU - T.J. Adler AU - N. Schreck AU - A. Kopp-Schneider AU - K. Maier-Hein AU - L. Maier-Hein AU - B.P. Müller-Stich AU - F. Nickel Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/11/25/2021.11.24.469943.abstract N2 - Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, it had been unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9,059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95 %). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decision making and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.Competing Interest StatementThe authors have declared no competing interest.HIShyperspectral imagingMSImultispectral imagingNASANational Aeronautics and Space AdministrationROIregion of interestSDstandard deviationt-SNEt-distributed Stochastic Neighbor Embedding ER -