# Whole-Sample Mapping of Cancerous and Benign Tissue Properties

## TL;DR

This paper introduces a high-resolution image registration method combining AFM stiffness measurements with histological images to distinguish cancerous from healthy tissue based on structural and mechanical properties.

## Contribution

It presents a novel image registration and mapping technique that localizes AFM measurements on tissue samples with high precision, enabling comprehensive stiffness mapping.

## Key findings

- Significant stiffness differences between healthy and cancerous liver tissue.
- High-accuracy localization of AFM measurements within 1.5 microns.
- Potential for improved early cancer detection through combined structural and mechanical analysis.

## Abstract

Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection. Atomic force microscopy (AFM) has been used to measure significant differences in stiffness between cancerous and healthy cells owing to its high force sensitivity and spatial resolution, however due to absorption and scattering of light, it is often challenging to accurately locate where AFM measurements have been made on a bulk tissue sample. In this paper we describe an image registration method that localizes AFM elastic stiffness measurements with high-resolution images of haematoxylin and eosin (H\&E)-stained tissue to within 1.5 microns. Color RGB images are segmented into three structure types (lumen, cells and stroma) by a neural network classifier trained on ground-truth pixel data obtained through k-means clustering in HSV color space. Using the localized stiffness maps and corresponding structural information, a whole-sample stiffness map is generated with a region matching and interpolation algorithm that associates similar structures with measured stiffness values. We present results showing significant differences in stiffness between healthy and cancerous liver tissue and discuss potential applications of this technique.