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

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.

Keywords: cancer; fibrillar collagen imaging; machine learning; mid-infrared spectral imaging; second harmonic generation; tumor microenvironment.