ArticleBiomolecules2020
Nerve Segmentation with Deep Learning from Label-Free Endoscopic Images Obtained Using Coherent Anti-Stokes Raman Scattering.
Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Semantic Segmentation for Pixel-Wise Visualization of Breast Cancer in Deep Ultraviolet-Excited Fluorescence Images.Acta histochemica et cytochemica · 2026Article
- Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x.BMC oral health · 2025Article
- Machine learning empowered coherent Raman imaging and analysis for biomedical applications.Communications engineering · 2025Review
- Development of a software system for surgical robots based on multimodal image fusion: study protocol.Frontiers in surgery · 2024Article
- Rapid azoospermia classification by stimulated Raman scattering and second harmonic generation microscopy.Biomedical optics express · 2023Article
- Review
- Application of Artificial Intelligence for Medical Research.Biomolecules · 2021Article
- Deep-Learning-Based Cerebral Artery Semantic Segmentation in Neurosurgical Operating Microscope Vision Using Indocyanine Green Fluorescence Videoangiography.Frontiers in neurorobotics · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Semantic segmentation with deep learning to extract nerves from label-free endoscopic images obtained using coherent anti-Stokes Raman scattering (CARS) for nerve-sparing surgery is described. We developed a CARS rigid endoscope in order to identify the exact location of peripheral nerves in surgery. Myelinated nerves are visualized with a CARS lipid signal in a label-free manner. Because the lipid distribution includes other tissues as well as nerves, nerve segmentation is required to achieve nerve-sparing surgery. We propose using U-Net with a VGG16 encoder as a deep learning model and pre-training with fluorescence images, which visualize the lipid distribution similar to CARS images, before fine-tuning with a small dataset of CARS endoscopy images. For nerve segmentation, we used 24 CARS and 1,818 fluorescence nerve images of three rabbit prostates. We achieved label-free nerve segmentation with a mean accuracy of 0.962 and an F 1 value of 0.860. Pre-training on fluorescence images significantly improved the performance of nerve segmentation in terms of the mean accuracy and F 1 value ( p < 0 . 05 ). Nerve segmentation of label-free endoscopic images will allow for safer endoscopic surgery, while reducing dysfunction and improving prognosis after surgery.
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Registered trials
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