Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Shaw, Michael and Claveau, Rémy and Manescu, Petru and Elmi, Muna and Brown, Biobele J and Scrimgeour, Ross and Kölln, Lisa S and McConnell, Gail and Fernandez‐Reyes, Delmiro (2021) Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films. Journal of Pathology, 255 (1). pp. 62-71. ISSN 0022-3417 (https://doi.org/10.1002/path.5738)

[thumbnail of Shaw-etal-JP-2021-Optical-mesoscopy-machine-learning-and-computational-microscopy-enable-high-information-content-diagnostic-imaging]
Preview
 Text. Filename: Shaw_etal_JP_2021_Optical_mesoscopy_machine_learning_and_computational_microscopy_enable_high_information_content_diagnostic_imaging.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (11MB)| Preview

Abstract

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high-resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high-resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification, we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood-borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope, with transformative potential for haematology and more generally across digital pathology.

ORCID iDs

Shaw, Michael, Claveau, Rémy, Manescu, Petru, Elmi, Muna, Brown, Biobele J, Scrimgeour, Ross ORCID logoORCID: https://orcid.org/0000-0003-1412-9748, Kölln, Lisa S, McConnell, Gail ORCID logoORCID: https://orcid.org/0000-0002-7213-0686 and Fernandez‐Reyes, Delmiro;
  • Item type:Article
    ID code:78073
    Dates:
    Date
    Event
    30 September 2021
    Published
    7 June 2021
    Published Online
    3 June 2021
    Accepted
    Subjects:Medicine
    Science > Physics
    Department:Faculty of Science > Physics
    Depositing user: Pure Administrator
    Date deposited:07 Oct 2021 13:40
    Last modified:11 Nov 2024 13:15
    URI:https://strathprints.strath.ac.uk/id/eprint/78073
CORE (COnnecting REpositories)