Open AccessCancer detection from stained biopsies using high-speed spectral imaging
Author(s) -
Eugene Brozgol,
Pramod Kumar,
Daniela Necula,
Irena Bronshtein-Berger,
Moshe Lindner,
Shlomi Medalion,
Lee Twito,
Yotam Shapira,
Helena Gondra,
Iris Barshack,
Yuval Garini
Publication year - 2022
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.445782
Subject(s) - spectral imaging , computer science , artificial intelligence , medical imaging , hyperspectral imaging , computer vision , cancer , pattern recognition (psychology) , medical physics , medicine , optics , physics
The escalating demand for diagnosing pathological biopsies requires the procedures to be expedited and automated. The existing imaging systems for measuring biopsies only measure color, and even though a lot of effort is invested in deep learning analysis, there are still serious challenges regarding the performance and validity of the data for the intended medical setting. We developed a system that rapidly acquires spectral images from biopsies, followed by spectral classification algorithms. The spectral information is remarkably more informative than the color information, and leads to very high accuracy in identifying cancer cells, as tested on tens of cancer cases. This was improved even more by using artificial intelligence algorithms that required a rather small training set, indicating the high level of information that exists in the spectral images. The most important spectral differences are observed in the nucleus and they are related to aneuploidy in tumor cells. Rapid spectral imaging measurement therefore can bridge the gap in the machine-aided diagnostics of whole biopsies, thus improving patient care, and expediting the treatment procedure.