Whole-Sample Mapping of Cancerous and Benign Tissue Properties
This work addresses the challenge of precise tissue property mapping for early cancer detection, representing an incremental improvement in medical imaging techniques.
The authors tackled the problem of accurately localizing atomic force microscopy (AFM) stiffness measurements on bulk tissue samples by developing an image registration method that aligns these measurements with high-resolution H&E-stained images to within 1.5 microns, enabling the generation of whole-sample stiffness maps that reveal significant differences between healthy and cancerous liver tissue.
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.