Quantitative analysis of second harmonic generated images of collagen fibers: a review

Abstract

Abstract Purpose The human body is a complex structure. Its strength is ensured by the collagen protein which exists under the form of fibers. The quantitative analysis of these fibers in biological tissues can be very interesting to establish a relationship between the microstructure and their functions. This analysis is usually performed using two-photon microscopy and second harmonic generated (SHG) images. Lately, more and more researchers focused on the use of SHG images since it is a non-invasive technique and allows the capture of collagen fibers only. Many image-processing techniques can be used to extract quantitative information from those images such as fiber orientations, dimensions, and density. Therefore, accurate measure extraction depends mainly on the used image processing methods and, thus, it is necessary to know what processing technique to use. Methods The main purpose of this article is to exhibit the most used techniques in collagen fiber quantitative analysis then categorize them according to the information to extract. A comparison of three most used methods in fiber orientation’s estimation is carried out. Result and conclusion Despite the considerable number of papers aiming to quantitatively analyze collagen fibers from SHG images, two main aspects were not deeply covered. First, the use of deep learning algorithms is still limited even for segmentation and denoizing applications. Second, most of the studies processed in this review focused on two-dimensional SHG images and did not take into consideration collagen fibers as a three-dimensional volume.