Environment-specific spectral modeling: A new tool for the analysis of biological specimens.
Giovanni ValbusaMartina CapozzaChiara BrioschiFrancesco BlasiSimona GhianiAlessandro MaiocchiPublished in: Journal of biophotonics (2018)
The recent discovery of fluorescent dyes for improving pathologic tissues identification has highlighted the need of robust methods for performance validation especially in the field of fluorescence-guided surgery. Optical imaging of excised tissue samples is the reference tool to validate the association between dyes localization and the underlying histology in a controlled environment. Spectral unmixing may improve the validation process discriminating dye from endogenous signal. Here, an innovative spectral modeling approach that weights the spectral shifts associated with changes in chemical environment is described. The method is robust against spectral shift variations and its application leads to unbiased spectral weights estimates as demonstrated by numerical simulations. Finally, spectral shifts values computed pixel-wise from spectral images are used to display additional information with potential diagnostic value.
Keyphrases
- optical coherence tomography
- dual energy
- high resolution
- minimally invasive
- small molecule
- healthcare
- magnetic resonance imaging
- magnetic resonance
- machine learning
- quantum dots
- deep learning
- convolutional neural network
- atrial fibrillation
- acute coronary syndrome
- photodynamic therapy
- rectal cancer
- human health
- label free
- fine needle aspiration