Collagen fibers play an important role in both the structure and function of various tissues in the human body. Visualization and quantitative measurements of collagen fibers are possible through imaging modalities such as second harmonic generation (SHG), but accurate segmentation of collagen fibers is difficult for datasets involving variable imaging depths due to the effects of scattering and absorption. Therefore, an objective approach to segmentation is needed for datasets with images of variable SHG intensity. In this study, a U-Net convolutional neural network (CNN) was trained to accurately segment collagen-positive pixels throughout SHG z-stacks. CNN performance was benchmarked against other common thresholding techniques, and was found to outperform intensity-based segmentation algorithms within an independent dataset, particularly at deeper imaging depths. These results indicate that a trained CNN can accurately segment collagen-positive pixels within a wide range of imaging depths, which is useful for quantitative SHG imaging in thick tissues.
Keywords: collagen; convolutional neural network; image segmentation; second harmonic generation.
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