The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
Abstract
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation. Combining multi-scale features is one of important factors for accurate segmentation. UNet++ was developed as a modified Unet by designing an architecture with nested and dense skip connections. However, it does not explore sufficient information from full scales and there is still a large room for improvement. In this paper, we propose a novel UNet 3+, which takes advantage of full-scale skip connections and deep supervisions. The full-scale skip connections incorporate low-level details with high-level semantics from feature maps in different scales; while the deep supervision learns hierarchical representations from the full-scale aggregated feature maps. The proposed method is especially benefiting for organs that appear at varying scales. In addition to accuracy improvements, the proposed UNet 3+ can reduce the network parameters to improve the computation efficiency. We further propose a hybrid loss function and devise a classification-guided module to enhance the organ boundary and reduce the over-segmentation in a non-organ image, yielding more accurate segmentation results. The effectiveness of the proposed method is demonstrated on two datasets. The code is available at: github.com/ZJUGiveLab/UNet-Version.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
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Fully convolutional networks for semantic segmentation
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Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
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UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00889-5_1 · Cited by 9,610
Rethinking Atrous Convolution for Semantic Image Segmentation
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Multiscale structural similarity for image quality assessment
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“The MS-SSIM loss function of p and g is defined as: (5) where 𝑀 is the total number of the scales, 𝜇 c , 𝜇 d and 𝜎 c , 𝜎 c are the mean and standard deviations of p and g, 𝜎 cd denotes their covariance.”
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Cited by
Swin-Unet: Unet-like pure transformer for medical image segmentation
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25066-8_9 · Cited by 5,640
MISSFormer: An Effective Transformer for 2D Medical Image Segmentation
Huang, X., Deng, Z., Li, D., Yuan, X., & Fu, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3230943 · Cited by 594
H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation
He, A., Wang, K., Li, T., Du, C., Xia, S., & Fu, H. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3264513 · Cited by 399
Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images
Li, R., Zheng, S., Zhang, C., Duan, C., Su, J., Wang, L., & Atkinson, P. M. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3093977 · Cited by 341
U-Net-Based Medical Image Segmentation
Yin, X.-X., Sun, L., Fu, Y., Lu, R., & Zhang, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4189781 · Cited by 318
Retracted
Metadata
- Authors
- Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., & Wu, J.
- Venue
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 1055-1059
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3015788359
- Cited by
- 2,774
- References
- 13
- FWCI
- 83.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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