Machine learning-assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.

Journal: Journal of biomedical optics
PMID: 39364328

Abstract

SIGNIFICANCE: Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues.

Authors

  • Wihan Adi
    University of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States.
  • Bryan E Rubio Perez
    University of Wisconsin-Madison, Department of Electrical and Computer Engineering, Madison, Wisconsin, United States.
  • Yuming Liu
    Center for Multiparametric Imaging of Tumor Immune Microenvironments, University of Minnesota and University of Wisconsin-Madison; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI.
  • Sydney Runkle
    University of Wisconsin-Madison, Department of Computer Science, Madison, Wisconsin, United States.
  • Kevin W Eliceiri
    Center for Multiparametric Imaging of Tumor Immune Microenvironments, University of Minnesota and University of Wisconsin-Madison; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI.
  • Filiz Yesilkoy
    University of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States.

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