Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks

Area of Science:

• Biomedical Imaging
• Computational Pathology
• Spectroscopic Analysis

Background:

• Current cancer detection methods like tissue biopsy are non-quantitative and prone to human error.
• Fourier transform infrared (FTIR) spectroscopic imaging offers a quantitative alternative but faces challenges with spectral artifacts and tissue heterogeneity.
• Traditional classification methods struggle with complex tissue compositions and subtle molecular differences.

Purpose of the Study:

• To develop and evaluate deep learning models, specifically Convolutional Neural Networks (CNNs), for improved classification of histological components using FTIR spectroscopic imaging.
• To investigate the combined use of spectral and spatial information for more accurate tissue analysis.
• To enhance diagnostic capabilities in cancer research and clinical practice.

Main Methods:

• Applied Convolutional Neural Networks (CNNs) with architectures designed for spectral and spatial data processing.
• Utilized FTIR spectroscopic imaging data from tissue microarrays (TMAs).
• Classified six major tissue constituents: adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts.

Main Results:

• CNNs integrating spatial and spectral information significantly outperformed per-pixel spectral classification.
• The approach enabled accurate classification of challenging cellular subtypes, like adipocytes, based on spatial characteristics.
• Demonstrated improved classifier performance for identifying diverse tissue components.

Conclusions:

• Deep learning algorithms, particularly CNNs, offer a powerful and efficient method for improving diagnostic accuracy in histopathology.
• Combining spectral and spatial data analysis enhances the identification of tissue components in FTIR spectroscopic imaging.
• This methodology holds significant potential for advancing cancer detection and research tools.