Open AccessDetection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra
Author(s) -
Masahiro Ishikawa,
Chisato Okamoto,
Kazuma Shinoda,
Hideki Kobara,
Chika Iwamoto,
Kenoki Ohuchida,
Makoto Hashizume,
A. Shimizu,
Naoki Kobayashi
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.004568
Subject(s) - hyperspectral imaging , stain , h&e stain , pathology , rgb color model , artificial intelligence , tissue microarray , pathological , computer science , pattern recognition (psychology) , staining , immunohistochemistry , medicine
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.