Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra

Masahiro Ishikawa, Chisato Okamoto, Kazuma Shinoda, Hideki Komagata, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume, Akinobu Shimizu, Naoki Kobayashi

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2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4568-4588
Number of pages21
JournalBiomedical Optics Express
Issue number9
Publication statusPublished - Sep 1 2019


All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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