ArticlePhysical and engineering sciences in medicine2022
Pulmonary nodule segmentation based on REMU-Net.
Article in Physical and engineering sciences in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- EGP-Net: a lung nodule segmentation network integrating edge guidance and pyramidal multi-scale contextual attention mechanisms.Frontiers in oncology · 2026Article
- VT-MFLV: Vision-Text Multimodal Feature Learning V Network for Medical Image Segmentation.Journal of imaging · 2025Article
- Dual-channel grouped cross-dimension attention V-Net for pulmonary nodule segmentation.Quantitative imaging in medicine and surgery · 2025Article
- A deep neural network improves endoscopic detection of laterally spreading tumors.Surgical endoscopy · 2025Article
- A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention.Scientific reports · 2024Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
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Authors and funding
3 authors.
Funding
Abstract
In recent years, U-Net has shown excellent performance in medical image segmentation, but it cannot accurately segment nodules of smaller size when segmenting pulmonary nodules. To make it more accurate to segment pulmonary nodules in CT images, U-Net is improved to REMU-Net. First, ResNeSt, which is the state-of-the-art ResNet variant, is used as the backbone of the U-Net, and a spatial attention module is introduced into the Split-Attention block of ResNeSt to enable the network to extract more diverse and efficient features. Secondly, a feature enhancement module based on the atrous spatial pyramid pooling (ASPP) is introduced in the U-Net, which is utilized to obtain more abundant context information. Finally, replacing the skip connection of the U-Net with a multi-scale skip connection overcomes the limitation that the decoder subnet can only accept same-scale feature information. Experiments show that REMU-Net has a Dice score of 84.76% on the LIDC-IDRI dataset. The network has better segmentation performance than most other existing U-Net improvement networks.
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