Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.
@cite_13 state to be the first to analyze SHG images with neural networks. They estimated the elastic properties of collagenous tissue. A classification or segmentation of fibers were not part of their investigation.
@cite_13
2016266030 2755176028 1980068389 2766462831
We present three-dimensional Fourier transform-second-harmonic generation (3D FT-SHG) imaging, a generalization of the previously reported two-dimensional FT-SHG, to quantify collagen fiber organization from 3D image stacks of biological tissues. The current implementation calculates 3D preferred orientation of a region of interest, and classifies regions of interest based on orientation anisotropy and average voxel intensity. Presented are some example applications of the technique which reveal the layered structure of collagen fibers in porcine sclera, and estimates the cut angle of porcine tendon tissues. This technique shows promising potential for studying biological tissues that contain fibrillar structures in 3D. Abstract 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 to develop a representative application. 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. Statement of Significance In this study, we developed, to our best knowledge, the first Deep Learning-based approach to estimate the elastic properties of collagenous tissues directly from noninvasive second harmonic generation images. The success of this study holds promise for the use of Machine Learning techniques to noninvasively and efficiently estimate the mechanical properties of many structure-based biological materials, and it also enables many potential applications such as serving as a quality control tool to select tissue for the manufacturing of medical devices (e.g. bioprosthetic heart valves). Abstract We propose the use of second-harmonic generation (SHG) microscopy for imaging collagen fibers in porcine femoral cortical bone. The technique is compared with scanning electron microscopy (SEM). SHG microscopy is shown to have excellent potential for bone imaging primarily due its intrinsic specificity to collagen fibers, which results in high contrast images without the need for specimen staining. Furthermore, this technique's ability to quantitatively assess collagen fiber organization is evaluated through an exploratory examination of bone structure as a function of age, from very young to mature bone. In particular, four different age groups: 1 month, 3.5 months, 6 months, and 30 months, were studied. Specifically, we employ the recently developed Fourier transform-second harmonic generation (FT-SHG) imaging technique for the quantification of the structural changes, and observe that as the bone develops, there is an overall reduction in porosity, the number of osteons increases, and the collagen fibers become comparatively more organized. It is also observed that the variations in structure across the whole cross-section of the bone increase with age. The results of this work show that quantitative SHG microscopy can serve as a valuable tool for evaluating the structural organization of collagen fibers in ex vivo bone studies. Abstract Second-harmonic generation imaging (SHG) captures triple helical collagen molecules near tissue surfaces. Biomedical research routinely utilizes various imaging software packages to quantify SHG signals for collagen content and distribution estimates in modern tissue samples including bone. For the first time using SHG, samples of modern, medieval, and ice age bones were imaged to test the applicability of SHG to ancient bone from a variety of ages, settings, and taxa. Four independent techniques including Raman spectroscopy, FTIR spectroscopy, radiocarbon dating protocols, and mass spectrometry-based protein sequencing, confirm the presence of protein, consistent with the hypothesis that SHG imaging detects ancient bone collagen. These results suggest that future studies have the potential to use SHG imaging to provide new insights into the composition of ancient bone, to characterize ancient bone disorders, to investigate collagen preservation within and between various taxa, and to monitor collagen decay regimes in different depositional environments.