Collagen is one of the most important proteins in mammals, conforming most animal tissues. This work explores how a basic collagen monomer unit 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 particle has a weak contrast and is relatively small. Possible attach fluorescence tags for contrast, but the size is still a problem for detecting orientation using fluorescence microscopy. This document presents a simulation of the visualization of collagen monomers and two methods for detecting monomer and classifying its orientation. A modify Gabor filter set, and an automatic classifier, trained by convolutional neuronal network (CNN), were used. By evaluating the performance of these two approaches compare to human observation, our results show that it is possible to determine the location and orientation of a single monomer with fluorescence microscopy. These findings can contribute to understanding collagen elements as collagen fibril.
|Title of host publication||Three-Dimensional and Multidimensional Microscopy|
|Subtitle of host publication||Image Acquisition and Processing XXVIII|
|Editors||Thomas G. Brown, Tony Wilson, Laura Waller|
|State||Published - 4 Mar 2021|
|Event||Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII 2021 - Virtual, Online, United States|
Duration: 6 Mar 2021 → 11 Mar 2021
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII 2021|
|Period||6/03/21 → 11/03/21|
Bibliographical noteFunding Information:
JR, CD, and SMS gratefully acknowledge partial funding from the United States National Institutes of Health under grant number 1R21EY029167-01. J.D. thankfully acknowledges funding from Project FONDECYT 1180685 (Comision Nacional de Investigacion Cientifica y Tecnologica Chile), the Advanced Center of Electrical and Electronic Engineering AC3E (CONICYT/FB0008) and from Fondo de Ayuda a la Investigacion (FAI), Universidad de los Andes.
© 2021 Copyright SPIE.
- computer vision
- fluorescence microscopy
- machine learning