ArticleJournal of biophotonics2022
Improved segmentation of collagen second harmonic generation images with a deep learning convolutional neural network.
Article in Journal of biophotonics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Second harmonic generation for brain imaging: pathology-related studies.Biophysical reviews · 2025Article
- Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks.Biomedical optics express · 2024Article
- Identifying and training deep learning neural networks on biomedical-related datasets.Briefings in bioinformatics · 2024Article
- Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist.Frontiers in cell and developmental biology · 2024Review
- Two-Photon Imaging for Non-Invasive Corneal Examination.Sensors (Basel, Switzerland) · 2022Review
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Authors and funding
2 authors.
Funding
Abstract
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.
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