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Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Wenbin Wu1, Guanjun Liu1,*, Kaiyi Liang2, Hui Zhou2

1 Tongji University, Shanghai, 201804, China
2 Jiading District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, 201800, China

* Corresponding Author: Guanjun Liu. Email: email

(This article belongs to the Special Issue: Deep Learning based Computational Methods for Abnormality Detection in Human Medical Images)

Computer Modeling in Engineering & Sciences 2023, 134(2), 1323-1335.


Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the latter one. In this article, we devise novel Inner Cascaded U-Net and Inner Cascaded U2-Net as improvements to plain cascaded U-Net for medical image segmentation. The proposed Inner Cascaded U-Net adds inner nested connections between two U-Nets to share more contextual information. To further boost segmentation performance, we propose Inner Cascaded U2-Net, which applies residual U-block to capture more global contextual information from different scales. The proposed models can be trained from scratch in an end-to-end fashion and have been evaluated on Multimodal Brain Tumor Segmentation Challenge (BraTS) 2013 and ISBI Liver Tumor Segmentation Challenge (LiTS) dataset in comparison to related U-Net, cascaded U-Net, U-Net++, U2-Net and state-of-the-art methods. Our experiments demonstrate that our proposed Inner Cascaded U-Net and Inner Cascaded U2-Net achieve better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as get finer outline segmentation.


Cite This Article

APA Style
Wu, W., Liu, G., Liang, K., Zhou, H. (2023). Inner cascaded u<sup>2</sup>-net: an improvement to plain cascaded u-net. Computer Modeling in Engineering & Sciences, 134(2), 1323-1335.
Vancouver Style
Wu W, Liu G, Liang K, Zhou H. Inner cascaded u<sup>2</sup>-net: an improvement to plain cascaded u-net. Comput Model Eng Sci. 2023;134(2):1323-1335
IEEE Style
W. Wu, G. Liu, K. Liang, and H. Zhou "Inner Cascaded U<sup>2</sup>-Net: An Improvement to Plain Cascaded U-Net," Comput. Model. Eng. Sci., vol. 134, no. 2, pp. 1323-1335. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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