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27 September 2024 Machine learning-assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues
Wihan Adi, Bryan E. Rubio Perez, Yuming Liu, Sydney Runkle, Kevin W. Eliceiri, Filiz Yesilkoy
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Journal of Biomedical Optics, Vol. 29, Issue 9, 093511 (September 2024). https://doi.org/10.1117/1.JBO.29.9.093511
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.

Aim

To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides an MIRSI method to detect fibrillar collagen based on its chemical signatures.

Approach

We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The other 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment.

Results

Compared with the SHG ground truth, the generated RF-MIRSI collagen images achieved a high average boundary F-score (0.8 at 4-pixel thresholds) in the collagen distribution, high correlation (Pearson’s R 0.82) in the collagen orientation, and similarly high correlation (Pearson’s R 0.66) in the collagen alignment.

Conclusions

We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Citation Download Citation
Wihan Adi, Bryan E. Rubio Perez, Yuming Liu, Sydney Runkle, Kevin W. Eliceiri, and Filiz Yesilkoy "Machine learning-assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues," Journal of Biomedical Optics 29(9), 093511 (27 September 2024). https://doi.org/10.1117/1.JBO.29.9.093511
Received: 22 May 2024; Accepted: 5 September 2024; Published: 27 September 2024
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KEYWORDS
Collagen
Second harmonic generation
Tissues
Education and training
Mid infrared
Biological samples
Machine learning
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