ArticleFrontiers in oncology2021
AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of AI-Driven Applications for Screening Nasopharyngeal Carcinoma Using MRI.Journal of magnetic resonance imaging : JMRI · 2026Pooled it
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- MambaGLA-NPC: a 3D framework integrating CNN and state space models for robust multi-center nasopharyngeal carcinoma segmentation.Frontiers in genome editing · 2026Article
- Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2024Review
- Swin Transformer-based automatic delineation of the hippocampus by MRI in hippocampus-sparing whole-brain radiotherapy.Frontiers in neuroscience · 2024Article
- MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.Frontiers in bioengineering and biotechnology · 2024Article
- CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images.Sensors (Basel, Switzerland) · 2023Article
- Article
- Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images.Sensors (Basel, Switzerland) · 2022Article
- CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation.Sensors (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
Radiotherapy is an essential method for treating nasopharyngeal carcinoma (NPC), and the segmentation of NPC is a crucial process affecting the treatment. However, manual segmentation of NPC is inefficient. Besides, the segmentation results of different doctors might vary considerably. To improve the efficiency and the consistency of NPC segmentation, we propose a novel AttR2U-Net model which automatically and accurately segments nasopharyngeal carcinoma from MRI images. This model is based on the classic U-Net and incorporates advanced mechanisms such as spatial attention, residual connection, recurrent convolution, and normalization to improve the segmentation performance. Our model features recurrent convolution and residual connections in each layer to improve its ability to extract details. Moreover, spatial attention is fused into the network by skip connections to pinpoint cancer areas more accurately. Our model achieves a DSC value of 0.816 on the NPC segmentation task and obtains the best performance compared with six other state-of-the-art image segmentation models.
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