Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Lotfollahi, Mahsa; Berisha, Sebastian; Daeinejad, Davar; Mayerich, David
• Is Part Of: Applied spectroscopy. Volume 73:Issue 5(2019)
• Page Start: 556
• Page End: 564
• Publication Date: 2019-05
• Subjects: Histology -- histopathology -- digital staining -- deep learning -- convolutional neural networks -- classification -- Fourier transform infrared -- FT-IR; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702818819857
• Languages: English
• ISSNs: 0003-7028

Abstract

Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.