TY - JOUR
T1 - Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra
AU - Ishikawa, Masahiro
AU - Okamoto, Chisato
AU - Shinoda, Kazuma
AU - Komagata, Hideki
AU - Iwamoto, Chika
AU - Ohuchida, Kenoki
AU - Hashizume, Makoto
AU - Shimizu, Akinobu
AU - Kobayashi, Naoki
N1 - Funding Information:
Japan Society for the Promotion of Science (JP 15K21336, JP 17K18064, JP15K21716).
Publisher Copyright:
© 2019 Optical Society of America.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420–750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
AB - Hyperspectral imaging (HSI) provides more detailed information than red-green-blue (RGB) imaging, and therefore has potential applications in computer-aided pathological diagnosis. This study aimed to develop a pattern recognition method based on HSI, called hyperspectral analysis of pathological slides based on stain spectrum (HAPSS), to detect cancers in hematoxylin and eosin-stained pathological slides of pancreatic tumors. The samples, comprising hyperspectral cubes of 420–750 nm, were harvested for HSI and tissue microarray (TMA) analysis. As a result of conducting HAPSS experiments with a support vector machine (SVM) classifier, we obtained maximal accuracy of 94%, a 14% improvement over the widely used RGB images. Thus, HAPSS is a suitable method to automatically detect tumors in pathological slides of the pancreas.
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U2 - 10.1364/BOE.10.004568
DO - 10.1364/BOE.10.004568
M3 - Article
AN - SCOPUS:85078699844
SN - 2156-7085
VL - 10
SP - 4568
EP - 4588
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 9
ER -
