DIGITAL STAINING OF HIGH-RESOLUTION FTIR SPECTROSCOPIC IMAGES

Histological stains, such as hemotaxylin and eosin (H& E), are commonly used to label tissue in clinical biopsies. However, these labels modify the tissue chemistry, making it difficult to use for further downstream analysis. Fourier transform infrared spectroscopy (FTIR) has shown promising results for characterizing disease-relevant tissues without chemical labels or dyes. However, tissue classification requires human annotation, which is difficult and tedious to acquire for complex samples. In addition, the results of a molecular analysis must be presented in a way that facilitates diagnosis for a trained pathologist. One proposed approach is digital staining, which uses machine learning to map an infrared spectroscopic image to the image that would be ideally produced with a chemical stain. While these methods produce promising results, the resolution is significantly lower than traditional histology. We demonstrate that high-resolution mappings can be obtained using FTIR imaging and histological staining of the same sample. In addition, we demonstrate that better results can be achieved with more recent convolutional neural networks (CNNs) that take advantage of both spatial and spectral features.