Presentation + Paper
20 June 2024 Analysing histology hyperspectral images: Does tissue thickness matter?
Author Affiliations +
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.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Javier Santana-Nunez, Laura Quintana-Quintana, Himar Fabelo, Samuel Ortega, Esther Sauras-Colón, Noèlia Gallardo-Borràs, Daniel Mata-Cano, Carlos López-Pablo, and Gustavo M. Callico "Analysing histology hyperspectral images: Does tissue thickness matter?", Proc. SPIE 13006, Biomedical Spectroscopy, Microscopy, and Imaging III, 1300611 (20 June 2024); https://doi.org/10.1117/12.3017010
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KEYWORDS
Biological samples

Tissues

Absorbance

Image segmentation

RGB color model

Absorption

Biopsy

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