Analysing Histology Hyperspectral Images: Does Tissue Thickness Matter?

Abstract

Cancer is one of the leading causes of death, thereby, contributing to their quick diagnosis or treatment is of greatest importance. Nowadays, tumours are mainly diagnosed and graded histologically using biopsies. Since the images need to be sharp to distinguish biological structures, samples are thinly sliced (3-5 μm) to avoid scattering and contrast is obtained using highly absorbance dyes (e.g., Haematoxylin and Eosin (H&E)). RGB (Red-Green-Blue) cameras have been widely employed to acquire those images, while new approaches, such as Hyperspectral (HS) Imaging (HSI), have been arising to obtain a greater amount of spectral information from the samples. However, in order to have diffuse light for the HS cameras to capture it, the thickness of the sample should be bigger than the ones employed in conventional microscopy. This work aims to characterize the influence of tissue thickness of histology breast samples sectioned at 2 and 3 μm on their spectral signatures. Based on the H&E transmittance spectra peaks, HS images were segmented into three structures: stroma (eosin-stained), nuclei (haematoxylin-stained), and background (non-stained). Results show that, spatially, in 3 μm samples there are more cells imaged than in 2 μm samples. Moreover, spectrally, 3 μm samples proportionate higher spectral contrast than 2 μm samples due the greater interaction of light with tissue, denoting them as more suitable for microscopic HSI.