A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases.

Gonzalo Rosa-Olmeda, Manuel Villa, Sara Hiller-Vallina, Miguel Chavarr��as, Fernando Pescador, Ricardo Gargini
Author Information
  1. Gonzalo Rosa-Olmeda: Research Center on Software Technologies and Multimedia Systems, Universidad Polit��cnica de Madrid, 28031 Madrid, Spain. ORCID
  2. Manuel Villa: Research Center on Software Technologies and Multimedia Systems, Universidad Polit��cnica de Madrid, 28031 Madrid, Spain. ORCID
  3. Sara Hiller-Vallina: Pathology and Neurooncology Unit, Instituto de Investigaci��n Biom��dicas I+12, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain. ORCID
  4. Miguel Chavarr��as: Research Center on Software Technologies and Multimedia Systems, Universidad Polit��cnica de Madrid, 28031 Madrid, Spain. ORCID
  5. Fernando Pescador: Research Center on Software Technologies and Multimedia Systems, Universidad Polit��cnica de Madrid, 28031 Madrid, Spain. ORCID
  6. Ricardo Gargini: Pathology and Neurooncology Unit, Instituto de Investigaci��n Biom��dicas I+12, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain. ORCID

Abstract

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology.

Keywords

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Grants

  1. PID2020-116417RB-C41/Ministerio de Ciencia, Innovaci��n y Universidades

MeSH Term

Microscopy
Software
Algorithms
Image Processing, Computer-Assisted
Humans
Databases, Factual
Hyperspectral Imaging

Word Cloud

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