All-in-focus fine needle aspiration biopsy imaging based on Fourier ptychographic microscopy
Abstract
Context: Cytology is the study of whole cells in diagnostic pathology. Unlike standard histologic thinly sliced specimens, cytologic preparations consist of preparations of whole cells where cells commonly cluster and aggregate. As such, cytology preparations are generally much thicker than histologic slides, resulting in large patches of defocus when examined under the microscope. A diagnostic aggregate of cells often cannot be viewed in focus together, requiring pathologists to continually manipulate the focal plane, complicating the task of accurately assessing the entire cellular aggregate and thus in making a diagnosis. Further, it is extremely difficult to acquire useful uniformly in-focus digital images of cytology preparations for applications such as remote diagnostic evaluations and artificial intelligence models. The predominant current method to address this issue is to acquire digital images at multiple focal planes of the entire slide, which demands long scanning time, complex and expensive scanning systems, and huge storage capacity. Aims: Here we report a unique imaging method that can acquire cytologic images efficiently and computationally render all-in-focus digital images that are highly compact. Methods and material: This method applies a metric-based digital refocusing to microscopy data collected with a Fourier ptychographic microscope (FPM). The digitally refocused patches of images are then synthesized into an all-in-focus image. Results: We report all-in-focus FPM results of thyroid fine needle aspiration (FNA) cytology samples, demonstrating our method's ability to overcome the height variance of 30 μm caused by cell aggregation, and rendering images at high resolution (corresponds to a standard microscope with objective NA of 0.75) and that are all-in-focus. Conclusions: This technology is applicable to standard microscopes, and we believe can have an impact on diagnostic accuracy as well as ease and speed of diagnosing challenging specimens. While we focus on cytology slides here, we anticipate this technology's advantages will translate well for histology applications. This technique also addresses the issue of remote rapid evaluation of cytology preparations. Finally, we believe that by resolving the focus heterogeneity issues in standard digital images, this technique is a critical advance for applying machine learning to cytology specimens.
Additional Information
© 2022 The Author(s). Published by Elsevier Inc. on behalf of Association for Pathology Informatics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Accepted 13 December 2021, Available online 30 June 2022, Version of Record 12 July 2022. The authors would like to thank Cheng Shen (California Institute of Technology) for imaging modality discussion, Dr. Sid Rawal and imaging team (Washington University School of Medicine) for sample preparation and imaging, and Prof. Mark Watson (Washington University School of Medicine) for pathology discussion. This work was supported by the National Institutes of Health [grant NIH-U01CA233363]. Source(s) of support: NIH-U01CA233363. Presentation at a meeting: Nil. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Attached Files
Published - 1-s2.0-S2153353922007131-main.pdf
Supplemental Material - 1-s2.0-S2153353922007131-mmc1.docx
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Additional details
- PMCID
- PMC9577034
- Eprint ID
- 115337
- DOI
- 10.1016/j.jpi.2022.100119
- Resolver ID
- CaltechAUTHORS:20220705-346652000
- NIH
- U01CA233363
- Created
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2022-07-08Created from EPrint's datestamp field
- Updated
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2023-07-06Created from EPrint's last_modified field