RT Journal Article
SR Electronic
T1 A Deep Learning Approach to Estimate Collagenous Tissue Nonlinear Anisotropic Stress-Strain Responses from Microscopy Images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 154161
DO 10.1101/154161
A1 Liang Liang
A1 Minliang Liu
A1 Wei Sun
YR 2017
UL http://biorxiv.org/content/early/2017/06/23/154161.abstract
AB Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen as a representative collagenous tissue. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images.