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Progress in Artificial Intelligence

Erschienen in:

27.02.2023 | Short Communication

SwinE-UNet3+: swin transformer encoder network for medical image segmentation

verfasst von: Ping Zou, Jian-Sheng Wu

Erschienen in: Progress in Artificial Intelligence | Ausgabe 1/2023

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Abstract

A SwinE-UNet3+ model is proposed to improve the problem that convolutional neural networks cannot capture long-range feature dependencies due to the limitation of receptive field and is insensitive to contour details in tumor segmentation tasks. Each encoder layer of SwinE-UNet3+ uses two consecutive Swin Transformer blocks to extract features, especially long-range features in images. Patch Merging is used for down-sampling between encoder layers. The decoder uses Conv2DTranspose to perform progressive up-sampling and uses convolution operation to aggregate the decoder information after up-sampling and the encoder information through skip connection. The proposed model evaluates the TipDM Cup rectal cancer dataset and the melanoma dermoscopic image ISIC-2017 dataset. Experimental results show that SwinE-UNet3+ model outperforms UNet, UNet++ and UNet3+ models in Dice coefficient, IOU value and Precision evaluation metric.

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Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2015). https://doi.org/10.1109/CVPR.2015.7298965CrossRef

Milletari, F., Navab, N., Ahmadi, S. A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016). https://doi.org/10.1109/3DV.2016.79

Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28

Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020). https://doi.org/10.1016/j.neunet.2019.08.025CrossRef

Li, X., Hao, C., Qi, X., Qi, D., Fu, C.W., Pheng-Ann, H.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)CrossRef

Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., Simulamet.: Resunet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225–2255 (2019). https://doi.org/10.1109/ISM46123.2019.00049

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11 (2018)

Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mcdonagh, S., Hammerla, N.Y., Kainz, B.: Attention U-Net: learning where to look for the pancreas. MIDL. In: Proc, pp. 1–10 (2018). https://doi.org/10.48550/arXiv.1804.03999

Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., Liu, J.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019). https://doi.org/10.1109/TMI.2019.2903562CrossRef

10.

Vaswani, A., Shazeer, N., Parmar, N.: Attention is all you need. Adv. Neural Inf. Process. Syst. 5998–6008 (2017)

11.

Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Tay, FE., Feng, J., Yan, S.: Tokens-to-token vit: training vision transformers from scratch on imagenet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 558–567 (2021). https://doi.org/10.48550/arXiv.2101.11986

12.

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformer. In: European Conference on Computer Vision, pp. 213–229 (2020). https://doi.org/10.1007/978-3-030-58452-8_13

13.

Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp. 36–46 (2021). https://doi.org/10.48550/arXiv.2102.10662

14.

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012–10022 (2021). https://doi.org/10.48550/arXiv.2103.14030

15.

Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. Arxiv Prep. (2021). https://doi.org/10.48550/arXiv.2105.05537CrossRef

16.

Huang, H., Lin, L., Tong, R., Hu, H., Wu, J.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059 (2020). https://doi.org/10.1109/ICASSP40776.2020.9053405

17.

Zeiler, M.D., Taylor, G.W., Fergus, R.: Adaptive deconvolutional networks for mid and high level feature learning. In: 2011 International Conference on Computer Vision, pp. 2018–2025 (2011). https://doi.org/10.1109/iccv.2011.6126474

18.

Jamieson, A.R., Drukker, K., Giger, M.L., Van Ginneken, B., Novak, C.L.: Breast image feature learning with adaptive deconvolutional networks. Proc. SPIE Int. Soc. Opt. Eng. 8315, 64–76 (2012). https://doi.org/10.1117/12.910710CrossRef

19.

Zhao, H., Jia, J., Koltun, V.: Exploring self-attention for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10076–10085 (2020). https://doi.org/10.1109/CVPR42600.2020.01009

20.

Petit, O., Thome, N., Rambour, C., Soler, L.: U-net transformer: self and cross attention for medical image segmentation. In: International Workshop on Machine Learning in Medical Imaging, pp. 267–276 (2021). https://doi.org/10.48550/arXiv.2103.06104

21.

Tang, Z., Jiang, W., Zhang, Z., Zhao, M., Zhang, L.: DenseNet with Up-sampling block for recognizing texts in images. Neural Comput. Appl. 32(11), 1–9 (2020). https://doi.org/10.1007/s00521-019-04285-8CrossRef

22.

Bipat, S., Glas, A.S., Slors, F.J.M., Zwinderman, A.H., Bossuyt, P.M.M., Stoker, J.: Rectal cancer: local staging and assessment of lymph node involvement with endoluminal US, CT, and MR imaging—a meta-analysis. Radiology 232(3), 773–783 (2004). https://doi.org/10.1148/radiol.2323031368CrossRef

23.

Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, pp. 168–172 (2018). https://doi.org/10.48550/arXiv.1710.05006

24.

Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1944). https://doi.org/10.2307/1932409CrossRef

25.

Kubota, T., Jerebko, A.K., Dewan, M., Salganicoff, M., Krishnan, A.: Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Anal. 15(1), 133–154 (2011). https://doi.org/10.1016/j.media.2010.08.005CrossRef

Titel: SwinE-UNet3+: swin transformer encoder network for medical image segmentation
verfasst von: Ping Zou
Jian-Sheng Wu
Publikationsdatum: 27.02.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 1/2023
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-023-00300-1

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