1 research outputs found
Whole-Sample Mapping of Cancerous and Benign Tissue Properties
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.Comment: Accepted at MICCAI201