Fourier ptychography microscopy for digital pathology

Fraser Eadie; Laura Copeland; Giuseppe Di Caprio; Gail McConnell; Akhil Kallepalli

doi:10.1111/jmi.70001

Fourier ptychography microscopy for digital pathology

Fraser Eadie^*
, Laura Copeland
, Giuseppe Di Caprio
, Gail McConnell
, Akhil Kallepalli^*

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

3 Citations (Scopus)

67 Downloads (Pure)

Abstract

Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high-resolution, and vast field-of-view capabilities. FPM is used in various medical applications across multiple optical wavelengths, from automated digital pathology to radiology and ultraviolet label-free imaging. This review explores the fundamental physical and computational concepts that have driven advancements in digital pathology using FPM. A crucial part of the progress has been the development of computational algorithms, which have directly contributed to the improvements in FPM. We evaluate early-stage algorithms like the Gerchberg-Saxton and highlight how phase-retrieval and deep-learning advancements have propelled FPM forward. Additionally, we discuss the impact of these algorithms on digital pathology for potential automated diagnosis, providing a comprehensive explanation of their influence on medical imaging and offering insights into future research directions.

Original language	English
Pages (from-to)	260-285
Number of pages	26
Journal	Journal of Microscopy
Volume	300
Issue number	2
Early online date	24 Jun 2025
DOIs	https://doi.org/10.1111/jmi.70001
Publication status	Published - 1 Nov 2025

Funding

The authors would like to thank the support from multiple sources for this work. FE acknowledges the PhD research support from the EPSRC Doctoral Training Partnership award [EP/W524670/1]. LC was supported by the Engineering and Physical Sciences Research Council (EP/X525017/1). GM was supported in part by the Biotechnology and Biological Sciences Research Council (BB/V019643/1, BB/X005178/1, BB/T011602/1, and BB/W019032/1) and the Leverhulme Trust. AK wishes to acknowledge the support of the Leverhulme Trust Early Career Fellowship and EPSRC funding to QuantIC [EP/M01326X/1].

Keywords

AI
deep learning
digital pathology
fluorescence
Fourier optics
medical imaging
phaseretrieval
polarisation

Access to Document

10.1111/jmi.70001Licence: CC BY 4.0

Eadie-etal-JM-2025-Fourier-ptychography-microscopy-for-digital-pathologyFinal published version, 14.6 MBLicence: CC BY 4.0

1 Active
2 Finished

DTP 2224 University of Strathclyde | Eadie, Fraser
Kallepalli, A. (Principal Investigator), Di Caprio, G. (Co-investigator) & Eadie, F. (Research Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/24 → 1/04/28
Project: Research Studentship - Internally Allocated
Spatial-temporal techniques for multi-dimensional imaging
Kallepalli, A. (Principal Investigator)
Leverhulme Trust
1/04/24 → 31/12/25
Project: Research Fellowship
Spatio-temporal techniques for multi-dimensional imaging
Kallepalli, A. (Principal Investigator)
1/01/23 → 31/12/25
Project: Research Fellowship

Chancellor's Fellowship
Kallepalli, A. (Recipient), 1 Apr 2024
Prize: Fellowship awarded competitively
Leverhulme Early Career Fellowship (2023-25)
Kallepalli, A. (Recipient), 2022
Prize: Fellowship awarded competitively

Optics and diagnostics for the clinic
Kallepalli, A. (Invited speaker)
25 Nov 2025
Activity: Talk or Presentation › Invited talk

Cite this

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title = "Fourier ptychography microscopy for digital pathology",

abstract = "Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high-resolution, and vast field-of-view capabilities. FPM is used in various medical applications across multiple optical wavelengths, from automated digital pathology to radiology and ultraviolet label-free imaging. This review explores the fundamental physical and computational concepts that have driven advancements in digital pathology using FPM. A crucial part of the progress has been the development of computational algorithms, which have directly contributed to the improvements in FPM. We evaluate early-stage algorithms like the Gerchberg-Saxton and highlight how phase-retrieval and deep-learning advancements have propelled FPM forward. Additionally, we discuss the impact of these algorithms on digital pathology for potential automated diagnosis, providing a comprehensive explanation of their influence on medical imaging and offering insights into future research directions.",

keywords = "AI, deep learning, digital pathology, fluorescence, Fourier optics, medical imaging, phaseretrieval, polarisation",

author = "Fraser Eadie and Laura Copeland and \{Di Caprio\}, Giuseppe and Gail McConnell and Akhil Kallepalli",

year = "2025",

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language = "English",

volume = "300",

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T1 - Fourier ptychography microscopy for digital pathology

AU - Eadie, Fraser

AU - Copeland, Laura

AU - Di Caprio, Giuseppe

AU - McConnell, Gail

AU - Kallepalli, Akhil

PY - 2025/11/1

Y1 - 2025/11/1

N2 - Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high-resolution, and vast field-of-view capabilities. FPM is used in various medical applications across multiple optical wavelengths, from automated digital pathology to radiology and ultraviolet label-free imaging. This review explores the fundamental physical and computational concepts that have driven advancements in digital pathology using FPM. A crucial part of the progress has been the development of computational algorithms, which have directly contributed to the improvements in FPM. We evaluate early-stage algorithms like the Gerchberg-Saxton and highlight how phase-retrieval and deep-learning advancements have propelled FPM forward. Additionally, we discuss the impact of these algorithms on digital pathology for potential automated diagnosis, providing a comprehensive explanation of their influence on medical imaging and offering insights into future research directions.

AB - Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high-resolution, and vast field-of-view capabilities. FPM is used in various medical applications across multiple optical wavelengths, from automated digital pathology to radiology and ultraviolet label-free imaging. This review explores the fundamental physical and computational concepts that have driven advancements in digital pathology using FPM. A crucial part of the progress has been the development of computational algorithms, which have directly contributed to the improvements in FPM. We evaluate early-stage algorithms like the Gerchberg-Saxton and highlight how phase-retrieval and deep-learning advancements have propelled FPM forward. Additionally, we discuss the impact of these algorithms on digital pathology for potential automated diagnosis, providing a comprehensive explanation of their influence on medical imaging and offering insights into future research directions.

KW - AI

KW - deep learning

KW - digital pathology

KW - fluorescence

KW - Fourier optics

KW - medical imaging

KW - phaseretrieval

KW - polarisation

U2 - 10.1111/jmi.70001

DO - 10.1111/jmi.70001

M3 - Review article

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ER -

Fourier ptychography microscopy for digital pathology

Abstract

Funding

Keywords

Access to Document

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Projects

DTP 2224 University of Strathclyde | Eadie, Fraser

Spatial-temporal techniques for multi-dimensional imaging

Spatio-temporal techniques for multi-dimensional imaging

Prizes

Chancellor's Fellowship

Leverhulme Early Career Fellowship (2023-25)

Activities

Optics and diagnostics for the clinic

Cite this