Exploring the role of sample thickness for hyperspectral microscopy tissue discrimination through Monte Carlo simulations

TL;DR

This study uses Monte Carlo simulations to analyze how sample thickness affects tissue discrimination in hyperspectral microscopy, revealing that thicker samples improve differentiation but reduce light intensity, emphasizing the importance of optimizing sample thickness.

Contribution

It is the first comprehensive simulation-based analysis of how sample thickness influences hyperspectral microscopy tissue discrimination.

Findings

Thicker samples (~500 micrometers) enhance tissue differentiation.

Thin samples reduce the ability to discriminate tissue types.

Increased thickness decreases light intensity, affecting image quality.

Abstract

Recent advancements in multispectral (MS) and hyperspectral (HS) microscopy have focused on sensor and system improvements, yet sample processing remains overlooked. We conducted an analysis of the literature, revealing that 40 percent of studies do not report sample thickness. Among those that did report it, the vast majority, 98 percent, used 2 to 10 micrometer samples. This study investigates the impact of unstained sample thickness on MS/HS image quality through light transport simulations. Monte Carlo simulations were conducted on various tissue types (i.e., breast, colorectal, liver, and lung). The simulations revealed that thin samples reduce tissue differentiation, while higher thicknesses (approximately 500 micrometers) improve discrimination, though at the cost of reduced light intensity. These findings highlight the need to study and optimize sample thickness for enhanced tissue characterization and diagnostic accuracy in MS/HS microscopy.