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000180692 1001_ $$aStudier-Fischer, Alexander$$b0
000180692 245__ $$aSpectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.
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000180692 520__ $$aVisual 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, the scientific community is lacking a comprehensive spectral tissue atlas, and it is 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 (9059 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 decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.
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000180692 650_2 $$2MeSH$$aAnimals
000180692 650_2 $$2MeSH$$aHyperspectral Imaging
000180692 650_2 $$2MeSH$$aMachine Learning
000180692 650_2 $$2MeSH$$aNeural Networks, Computer
000180692 650_2 $$2MeSH$$aSwine
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000180692 7001_ $$aÖzdemir, Berkin$$b3
000180692 7001_ $$0P:(DE-He78)1042737c83ba70ec508bdd99f0096864$$aWiesenfarth, Manuel$$b4$$udkfz
000180692 7001_ $$00000-0002-3574-2085$$aAyala, Leonardo$$b5
000180692 7001_ $$aOdenthal, Jan$$b6
000180692 7001_ $$00000-0001-5798-8003$$aKnödler, Samuel$$b7
000180692 7001_ $$00000-0003-2931-6247$$aKowalewski, Karl Friedrich$$b8
000180692 7001_ $$00000-0002-4209-9470$$aHaney, Caelan Max$$b9
000180692 7001_ $$00000-0001-9653-2789$$aCamplisson, Isabella$$b10
000180692 7001_ $$00000-0003-0960-038X$$aDietrich, Maximilian$$b11
000180692 7001_ $$00000-0001-8373-9406$$aSchmidt, Karsten$$b12
000180692 7001_ $$00000-0002-3964-3527$$aSalg, Gabriel Alexander$$b13
000180692 7001_ $$aKenngott, Hannes Götz$$b14
000180692 7001_ $$0P:(DE-He78)ae131915396ed2f27752c043e123897e$$aAdler, Tim$$b15$$udkfz
000180692 7001_ $$0P:(DE-He78)0d054b6843ace36d1c965b6cb938d1c9$$aSchreck, Nicholas$$b16$$udkfz
000180692 7001_ $$0P:(DE-He78)bb6a7a70f976eb8df1769944bf913596$$aKopp-Schneider, Annette$$b17$$udkfz
000180692 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b18$$udkfz
000180692 7001_ $$0P:(DE-He78)26a1176cd8450660333a012075050072$$aMaier-Hein, Lena$$b19$$udkfz
000180692 7001_ $$aMüller-Stich, Beat Peter$$b20
000180692 7001_ $$00000-0001-6066-8238$$aNickel, Felix$$b21
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