IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-55262-2.html
   My bibliography  Save this article

Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning

Author

Listed:
  • Eunwoo Park

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH))

  • Sampa Misra

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH))

  • Dong Gyu Hwang

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH))

  • Chiho Yoon

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH))

  • Joongho Ahn

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH)
    Opticho Inc)

  • Donggyu Kim

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH))

  • Jinah Jang

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH))

  • Chulhong Kim

    (Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH)
    Pohang University of Science and Technology (POSTECH)
    Opticho Inc)

Abstract

Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional confocal fluorescence microscopy. Here, we demonstrate an explainable deep learning-based unsupervised inter-domain transformation of low-resolution unlabeled mid-infrared photoacoustic microscopy images into confocal-like virtually fluorescence-stained high-resolution images. The explainable deep learning-based framework is proposed for this transformation, wherein an unsupervised generative adversarial network is primarily employed and then a saliency constraint is added for better explainability. We validate the performance of explainable deep learning-based mid-infrared photoacoustic microscopy by identifying cell nuclei and filamentous actins in cultured human cardiac fibroblasts and matching them with the corresponding CFM images. The XDL ensures similar saliency between the two domains, making the transformation process more stable and more reliable than existing networks. Our XDL-MIR-PAM enables label-free high-resolution duplexed cellular imaging, which can significantly benefit many research avenues in cell biology.

Suggested Citation

  • Eunwoo Park & Sampa Misra & Dong Gyu Hwang & Chiho Yoon & Joongho Ahn & Donggyu Kim & Jinah Jang & Chulhong Kim, 2024. "Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55262-2
    DOI: 10.1038/s41467-024-55262-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-55262-2
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-55262-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Seonghee Cho & Minsu Kim & Joongho Ahn & Yeonggeun Kim & Junha Lim & Jeongwoo Park & Hyung Ham Kim & Won Jong Kim & Chulhong Kim, 2024. "An ultrasensitive and broadband transparent ultrasound transducer for ultrasound and photoacoustic imaging in-vivo," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chaorui Qiu & Zhiqiang Zhang & Zhiqiang Xu & Liao Qiao & Li Ning & Shujun Zhang & Min Su & Weichang Wu & Kexin Song & Zhuo Xu & Long-Qing Chen & Hairong Zheng & Chengbo Liu & Weibao Qiu & Fei Li, 2024. "Transparent ultrasonic transducers based on relaxor ferroelectric crystals for advanced photoacoustic imaging," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55262-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.