Structural analysis of U-Net and its variants in the field of medical image segmentation

Research Article
Open access

Structural analysis of U-Net and its variants in the field of medical image segmentation

Yanjia Kan 1*
  • 1 Xi'an Jiaotong-liverpool University    
  • *corresponding author Yanjia.Kan20@student.xjtlu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230800
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

Medical image segmentation can provide valuable information for doctors, it has important research value in the medical field. Meanwhile, U-Net, as the fundamental networks for such tasks, brings a substantial improvement in the segmentation performance of traditional medical images. With the increasingly widespread use of U-Net, researchers have designed various U-Net variants according to different task requirements. However, most of the current summaries of U-Net variants are divided according to the direction of network applications, and the structural relationship between the variant networks and U-Net is not elaborated. Therefore, this paper classifies U-Net variants according to their network framework by elaborating the principles of U-Net structure. According to the U-Net network structure, it is divided into three main categories: backbone improvement, module addition and cross-network fusion. Further, the characteristics, advantages and disadvantages of different categories of variants are introduced, and the directions of the variants for U-Net optimization are analyzed. Finally, the article summarizes the current development direction of U-Net variants and provides an outlook on the future directions that can continue to be optimized.

Keywords:

U-Net, medical image segmentation, U-Net variants

Kan,Y. (2023). Structural analysis of U-Net and its variants in the field of medical image segmentation. Applied and Computational Engineering,15,1-10.
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References

[1]. Long, J., Shelhamer, E., & Darrell, T. Fully convolutional networks for semantic segmentation. 2015, Computer Vision and Pattern Recognition. 3431-3440.

[2]. Ronneberger, O., Fischer, P., & Brox, T. U-net: Convolutional networks for biomedical image segmentation. 2015, In Medical Image Computing and Computer-Assisted Intervention, 234-241.

[3]. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. 2016, In Medical Image Computing and Computer-Assisted Intervention 424-432.

[4]. Milletari, F., Navab, N., & Ahmadi, S. A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. 2016, In 3DV. 565-571.

[5]. Xiao, X., Lian, S., Luo, Z., & Li, S. Weighted res-unet for high-quality retina vessel segmentation. 2018, In International Textile Machinery Exhibition 327-331.

[6]. Ibtehaz, N., & Rahman, M. S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. 2020, Neural networks, 121, 74-87.

[7]. Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. 2018, arXiv preprint arXiv:1802.06955.

[8]. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. Unet++: A nested u-net architecture for medical image segmentation. 2018, In Deep Learning on Medical Image Analysis 3-11.

[9]. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., ... & Wu, J. Unet 3+: A full-scale connected unet for medical image segmentation. 2019 In International Conference on Acoustics, Speech and Signal Processing 1055-1059.

[10]. Xiang, T., Zhang, C., Liu, D., Song, Y., Huang, H., & Cai, W. BiO-Net: learning recurrent bi-directional connections for encoder-decoder architecture. 2020 In Medical Image Computing and Computer-Assisted Intervention. 74-84.

[11]. Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., & Rueckert, D. Attention u-net: Learning where to look for the pancreas. 2018, arXiv preprint arXiv:1804.03999.

[12]. Azad, R., Asadi-Aghbolaghi, M., Fathy, M., & Escalera, S. Bi-directional ConvLSTM U-Net with densley connected convolutions. 2019, International conference on computer vision workshops.

[13]. Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. Doubleu-net: A deep convolutional neural network for medical image segmentation. In Conference Board of the Mathematical Sciences 558-564.

[14]. Soltanpour, M., Greiner, R., Boulanger, P., & Buck, B. Ischemic stroke lesion prediction in ct perfusion scans using multiple parallel u-nets following by a pixel-level classifier. 2019 In Biological Information and Biomedical Engineering. 957-963.

[15]. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. Transunet: Transformers make strong encoders for medical image segmentation. 2019 arXiv preprint arXiv:2102.04306.

[16]. Chen, X., Li, Y., Yao, L., Adeli, E., & Zhang, Y. Generative adversarial U-Net for domain-free medical image augmentation. 2021 arXiv preprint arXiv:2101.04793.

Cite this article

Kan,Y. (2023). Structural analysis of U-Net and its variants in the field of medical image segmentation. Applied and Computational Engineering,15,1-10.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-021-9​(Print) / 978-1-83558-022-6(Online)
Editor：Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.15
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Long, J., Shelhamer, E., & Darrell, T. Fully convolutional networks for semantic segmentation. 2015, Computer Vision and Pattern Recognition. 3431-3440.

[2]. Ronneberger, O., Fischer, P., & Brox, T. U-net: Convolutional networks for biomedical image segmentation. 2015, In Medical Image Computing and Computer-Assisted Intervention, 234-241.

[3]. Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. 2016, In Medical Image Computing and Computer-Assisted Intervention 424-432.

[4]. Milletari, F., Navab, N., & Ahmadi, S. A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. 2016, In 3DV. 565-571.

[5]. Xiao, X., Lian, S., Luo, Z., & Li, S. Weighted res-unet for high-quality retina vessel segmentation. 2018, In International Textile Machinery Exhibition 327-331.

[6]. Ibtehaz, N., & Rahman, M. S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. 2020, Neural networks, 121, 74-87.

[7]. Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. 2018, arXiv preprint arXiv:1802.06955.

[8]. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. Unet++: A nested u-net architecture for medical image segmentation. 2018, In Deep Learning on Medical Image Analysis 3-11.

[9]. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., ... & Wu, J. Unet 3+: A full-scale connected unet for medical image segmentation. 2019 In International Conference on Acoustics, Speech and Signal Processing 1055-1059.

[10]. Xiang, T., Zhang, C., Liu, D., Song, Y., Huang, H., & Cai, W. BiO-Net: learning recurrent bi-directional connections for encoder-decoder architecture. 2020 In Medical Image Computing and Computer-Assisted Intervention. 74-84.

[11]. Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., & Rueckert, D. Attention u-net: Learning where to look for the pancreas. 2018, arXiv preprint arXiv:1804.03999.

[12]. Azad, R., Asadi-Aghbolaghi, M., Fathy, M., & Escalera, S. Bi-directional ConvLSTM U-Net with densley connected convolutions. 2019, International conference on computer vision workshops.

[13]. Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. Doubleu-net: A deep convolutional neural network for medical image segmentation. In Conference Board of the Mathematical Sciences 558-564.

[14]. Soltanpour, M., Greiner, R., Boulanger, P., & Buck, B. Ischemic stroke lesion prediction in ct perfusion scans using multiple parallel u-nets following by a pixel-level classifier. 2019 In Biological Information and Biomedical Engineering. 957-963.

[15]. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. Transunet: Transformers make strong encoders for medical image segmentation. 2019 arXiv preprint arXiv:2102.04306.

[16]. Chen, X., Li, Y., Yao, L., Adeli, E., & Zhang, Y. Generative adversarial U-Net for domain-free medical image augmentation. 2021 arXiv preprint arXiv:2101.04793.

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