ArticlePattern recognition2022
Super U-Net: a modularized generalizable architecture.
Article in Pattern recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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Who cites it
22 citing papers in PubMed.
- RCTUnet: a deep learning model for crop-residue-soil image segmentation and crop residue cover extraction.Journal of Zhejiang University. Science. B · 2026Article
- Article
- Animal models of benign airway stenosis: Advances in construction techniques, evaluation systems, and perspectives.Animal models and experimental medicine · 2026Review
- RGB-based visual encoding of vibration data for gearbox fault diagnosis using U-Net segmentation model.PloS one · 2026Article
- Beyond nodules: body composition as a biomarker for future lung cancer.European radiology · 2025Article
- Automatic modeling of high genus geological bodies using improved EdgeConnect and deep plug and play super resolution GAN.Scientific reports · 2025Article
- Utilizing Artificial Intelligence for CSF Segmentation and Analysis in Head CT Imaging: A Systematic Review.Brain sciences · 2025Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT.Sensors (Basel, Switzerland) · 2025Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- AI-driven optimization of agricultural water management for enhanced sustainability.Scientific reports · 2024Article
- MCSE-U-Net: multi-convolution blocks and squeeze and excitation blocks for vessel segmentation.Quantitative imaging in medicine and surgery · 2024Article
- STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.Medical image analysis · 2023Article
- Predicting left/right lung volumes, thoracic cavity volume, and heart volume from subject demographics to improve lung transplant.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet.Scientific reports · 2023Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module.Bioengineering (Basel, Switzerland) · 2023Article
- CT-derived body composition associated with lung cancer recurrence after surgery.Lung cancer (Amsterdam, Netherlands) · 2023Article
- Integrating spectral and image information for prediction of cottonseed vitality.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Objective: To develop and validate a novel convolutional neural network (CNN) termed "Super U-Net" for medical image segmentation. Methods: Super U-Net integrates a dynamic receptive field module and a fusion upsampling module into the classical U-Net architecture. The model was developed and tested to segment retinal vessels, gastrointestinal (GI) polyps, skin lesions on several image types (i.e., fundus images, endoscopic images, dermoscopic images). We also trained and tested the traditional U-Net architecture, seven U-Net variants, and two non-U-Net segmentation architectures. K-fold cross-validation was used to evaluate performance. The performance metrics included Dice similarity coefficient (DSC), accuracy, positive predictive value (PPV), and sensitivity. Results: Super U-Net achieved average DSCs of 0.808±0.0210, 0.752±0.019, 0.804±0.239, and 0.877±0.135 for segmenting retinal vessels, pediatric retinal vessels, GI polyps, and skin lesions, respectively. The Super U-net consistently outperformed U-Net, seven U-Net variants, and two non-U-Net segmentation architectures (p < 0.05). Conclusion: Dynamic receptive fields and fusion upsampling can significantly improve image segmentation performance.
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Registered trials
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.