The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Recurrent residual U-Net for medical image segmentation
Open accessAbstract
Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architectures with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U-Net.
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Cites
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VM-UNet: Vision Mamba UNet for Medical Image Segmentation
Ruan, J., Li, J., & Xiang, S. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3767748 · Cited by 804
A review of multimodal medical image fusion techniques
Huang, B., Yang, F., Yin, M., Mo, X., & Zhong, C. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/8279342 · Cited by 220
DLPacker : Deep learning for prediction of amino acid side chain conformations in proteins
Misiura, M., Shroff, R., Thyer, R., & Kolomeisky, A. B. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26311 · Cited by 51
Metadata
- Authors
- Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K.
- Venue
- Journal of Medical Imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2019-03-27
- Volume
- 6
- Issue
- 01
- Pages
- 1
- ISSN
- 2329-4302 (ISSN-L)
- OpenAlex
- W2923997689
- Cited by
- 850
- References
- 16
- FWCI
- 34.41
- Type
- journal article
- Language
- en
- Open access
- green
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- Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019). Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging. https://doi.org/10.1117/1.jmi.6.1.014006
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