Significance: Collagen is the most abundant protein in vertebrates and is found in tissues that regularly experience tension, compression, and shear forces. However, the underlying mechanism of collagen fibril formation and remodeling is poorly understood. Aim: We explore how a collagen monomer is visualized using fluorescence microscopy and how its spatial orientation is determined. Defining the orientation of collagen monomers is not a trivial problem, as the monomer has a weak contrast and is relatively small. It is possible to attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy. Approach: We present two methods for detecting a monomer and classifying its orientation. A modified Gabor filter set and an automatic classifier trained by convolutional neural network based on a synthetic dataset were used. Results: By evaluating the performance of these two approaches with synthetic and experimental data, our results show that it is possible to determine the location and orientation with an error of 1/437 deg of a single monomer with fluorescence microscopy. Conclusions: These findings can contribute to our understanding of collagen monomers interaction with collagen fibrils surface during fibril formation and remodeling.
|Journal||Journal of Biomedical Optics|
|State||Published - 31 Jul 2021|
Bibliographical notePublisher Copyright:
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
- convolutional neural network
- fluorescence microscopy
- Gabor filter
- monomer orientation
- Neural Networks, Computer
- Extracellular Matrix
- Microscopy, Fluorescence