ArticleQuantitative imaging in medicine and surgery2020
Dense-UNet: a novel multiphoton
Article in Quantitative imaging in medicine and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it, 264 citations in OpenAlex.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- A Hybrid U-Shaped Deep Learning Network for Intracerebral Hemorrhage Segmentation in CT Scans.Sensors (Basel, Switzerland) · 2026Article
- IPSM-UNet: An Inverted Pyramid-Shaped U-Net++ Architecture with Multi-Resolution Information Interaction for Coronary Artery Segmentation.Journal of imaging · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Oil spill segmentation of noisy SAR images using domain adaptation based UNet.Scientific reports · 2026Article
- LTPNet: Lesion-Aware Triple-Path Feature Fusion Network for Skin Lesion Segmentation.Journal of imaging · 2026Article
- SSL-DA: Semi-and Self-Supervised Learning with Dual Attention for Echocardiogram Segmentation.Journal of imaging informatics in medicine · 2026Article
- AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation.Journal of imaging informatics in medicine · 2026Article
- Deep Learning-Based Liver Tumor Segmentation from Computed Tomography Scans with a Gradient-Enhanced Network.Diagnostics (Basel, Switzerland) · 2026Article
- BOUNDARY-AWARE INSTANCE SEGMENTATION IN MICROSCOPY IMAGING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- SCEAF-UNet: Medical image segmentation based on spatial-channel feature enhancement and adaptive fusion.PloS one · 2026Article
- Performance Comparison of U-Net and Its Variants for Carotid Intima-Media Segmentation in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2025Article
- Human Detection in UAV Thermal Imagery: Dataset Extension and Comparative Evaluation on Embedded Platforms.Journal of imaging · 2025Article
- Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.Scientific reports · 2025Article
- Federated learning with integrated attention multiscale model for brain tumor segmentation.Scientific reports · 2025Article
- Quantitative assessment ofBiomedical optics express · 2025Article
- Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A MrOS Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study.JMIR aging · 2025Article
- A Two stage deep learning network for automated femoral segmentation in bilateral lower limb CT scans.Scientific reports · 2025Article
- Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.Journal of imaging informatics in medicine · 2025Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiphoton microscopy (MPM) offers a feasible approach for the biopsy in clinical medicine, but it has not been used in clinical applications due to the lack of efficient image processing methods, especially the automatic segmentation technology. Segmentation technology is still one of the most challenging assignments of the MPM imaging technique.
methodsThe MPM imaging segmentation model based on deep learning is one of the most effective methods to address this problem. In this paper, the practicability of using a convolutional neural network (CNN) model to segment the MPM image of skin cells
resultsSixty training images were taken from the dorsal forearm using a femtosecond Ti:Sa laser running at 735 nm. The resolution of the images is 128×128 pixels. Experimental results confirmed that the accuracy of Dense-UNet (92.54%) was higher than that of U-Net (88.59%), with a significantly lower loss value of 0.1681. The 90.60% Dice coefficient value of Dense-UNet outperformed U-Net by 11.07%. The F1-Score of Dense-UNet, U-Net, and Seg-Net was 93.35%, 90.02%, and 85.04%, respectively.
conclusionsThe deepened down-sampling path improved the ability of the model to capture cellular fined-detailed boundary features, while the symmetrical up-sampling path provided a more accurate location based on the test result. These results were the first time that the segmentation of MPM
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.