Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images

Samuel Ortega, Martin Halicek, Himar Fabelo, Raul Guerra, Carlos Lopez, Marylene Lejaune, Fred Godtliebsen, Gustavo M. Callico, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In recent years, hyperspectral imaging (HSI) has been shown as a promising imaging modality to assist pathologists in the diagnosis of histological samples. In this work, we present the use of HSI for discriminating between normal and tumor breast cancer cells. Our customized HSI system includes a hyperspectral (HS) push-broom camera, which is attached to a standard microscope, and home-made software system for the control of image acquisition. Our HS microscopic system works in the visible and near-infrared (VNIR) spectral range (400 - 1000 nm). Using this system, 112 HS images were captured from histologic samples of human patients using 20× magnification. Cell-level annotations were made by an expert pathologist in digitized slides and were then registered with the HS images. A deep learning neural network was developed for the HS image classification, which consists of nine 2D convolutional layers. Different experiments were designed to split the data into training, validation and testing sets. In all experiments, the training and the testing set correspond to independent patients. The results show an area under the curve (AUCs) of more than 0.89 for all the experiments. The combination of HSI and deep learning techniques can provide a useful tool to aid pathologists in the automatic detection of cancer cells on digitized pathologic images.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510634077
DOIs
StatePublished - 2020
Externally publishedYes
EventMedical Imaging 2020: Digital Pathology - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11320
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Digital Pathology
Country/TerritoryUnited States
CityHouston
Period2/19/202/20/20

Keywords

  • Deep learning
  • Histological
  • Hyperspectral
  • Microscopy

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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