Thyroid nodule segmentation method integrating receiving weighted key-value architecture and spherical geometric features_Journal of Biomedical Engineering

Authors：

ZHU Licheng ,  WEI Guohui

College of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China;

Corresponding author：

WEI Guohui, Email: bmie530@163.com

Keywords：

Thyroid nodules; Image segmentation; Receiving weighted key-value architecture; Spherical geometric feature; Feature fusion

DOI：

10.7507/1001-5515.202412009

Video：

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To address the high computational complexity of the Transformer in the segmentation of ultrasound thyroid nodules and the loss of image details or omission of key spatial information caused by traditional image sampling techniques when dealing with high-resolution, complex texture or uneven density two-dimensional ultrasound images, this paper proposes a thyroid nodule segmentation method that integrates the receiving weighted key-value (RWKV) architecture and spherical geometry feature (SGF) sampling technology. This method effectively captures the details of adjacent regions through two-dimensional offset prediction and pixel-level sampling position adjustment, achieving precise segmentation. Additionally, this study introduces a patch attention module (PAM) to optimize the decoder feature map using a regional cross-attention mechanism, enabling it to focus more precisely on the high-resolution features of the encoder. Experiments on the thyroid nodule segmentation dataset (TN3K) and the digital database for thyroid images (DDTI) show that the proposed method achieves dice similarity coefficients (DSC) of 87.24% and 80.79% respectively, outperforming existing models while maintaining a lower computational complexity. This approach may provide an efficient solution for the precise segmentation of thyroid nodules.

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24.	Wei J, Wang S, Huang Q. F³Net: fusion, feedback and focus for salient object detection//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(7): 12321-12328.
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26.	曹玉红, 徐海, 刘荪傲, 等. 基于深度学习的医学影像分割研究综述. 计算机应用, 2021, 41(8): 2273-2287.
27.	Ma X, Sun B, Liu W, et al. AMSeg: a novel adversarial architecture based multi-scale fusion framework for thyroid nodule segmentation. IEEE Access, 2023, 11: 72911-72924.
28.	Chen J, Lu Y, Yu Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
29.	Bi H, Cai C, Sun J, et al. BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation. Computer Methods and Programs in Biomedicine, 2023, 238: 107614.
30.	Zhang C, Wei G, Qiu M. HCMUnet: a hybrid CNN and Mamba network for medical ultrasound image segmentation. Preprint, 2024, http://dx.doi.org/10.2139/ssrn.4920609.
31.	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas. arXiv preprint, 2018, arXiv: 1804.03999.
32.	Huang X, Deng Z, Li D, et al. MISSFormer: an effective medical image segmentation transformer. arXiv preprint 2021, arXiv: 2109.07162.

1. Chi J, Yu X, Zhang Y. Thyroid nodule malignantrisk detection in ultrasound image by fusing deep and texture features. Journal of Image and Graphics, 2018, 23(10): 1582-1593.
2. Welker M J, Orlov D. Thyroid nodules. American Family Physician, 2003, 67(3): 559-567.
3. Hu Y, Qin P, Zeng J, et al. Ultrasound thyroid segmentation based on segmented frequency domain and local attention. Journal of Image and Graphics, 2020, 25(10): 2195-2205.
4. Wang J, Huang Q, Tang F, et al. Stepwise feature fusion: local guides global//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022: 110-120.
5. Tang F, Xu Z, Huang Q, et al. DuAT: dual-aggregation transformer network for medical image segmentation//Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Singapore: Springer Nature Singapore, 2023: 343-356.
6. Ying X, Yu Z, Yu R, et al. Thyroid nodule segmentation in ultrasound images based on cascaded convolutional neural network//Neural Information Processing: 25th International Conference (ICONIP 2018), Springer International Publishing, 2018: 373-384.
7. Buda M, Wildman-Tobriner B, Castor K, et al. Deep learning-based segmentation of nodules in thyroid ultrasound: improving performance by utilizing markers present in the images. Ultrasound in Medicine & Biology, 2020, 46(2): 415-421.
8. Ouahabi A, Taleb-Ahmed A. RETRACTED: deep learning for real-time semantic segmentation: Application in ultrasound imaging. Pattern Recognition Letters, 2021: 27-34.
9. Pan H, Zhou Q, Latecki L J. SGUNET: semantic guided Unet for thyroid nodule segmentation//2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021: 630-634.
10. Peng Y, Yu D, Guo Y. MShNet: multi-scale feature combined with h-network for medical image segmentation. Biomedical Signal Processing and Control, 2023, 79: 104167.
11. Kang Q, Lao Q, Li Y, et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical Image Analysis, 2022, 79: 102443.
12. 刘文婷, 卢新明. 基于计算机视觉的Transformer研究进展. 计算机工程与应用, 2022, 58(6): 1-16.
13. Strudel R, Garcia R, Laptev I, et al. Segmenter: transformer for semantic segmentation//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 7262-7272.
14. Chi J, Li Z, Sun Z. et al. Hybrid transformer UNet for thyroid segmentation from ultrasound scans. Computers in Biology and Medicine, 2023, 153: 106453.
15. Li C, Du R, Luo Q. et al. A novel model of thyroid nodule segmentation for ultrasound images. Ultrasound in Medicine & Biology, 2023, 49(2): 489-496.
16. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale. arXiv preprint, 2020, arXiv: 2010.11929.
17. 龚声蓉, 刘纯平, 王强. 图像分割技术. 数字图像处理与分析, 2006: 183-211.
18. Peng B, Alcaide E, Anthony Q, et al. RWKV: reinventing RNNs for the transformer era. arXiv preprint, 2023, arXiv: 2305.13048.
19. Zhuang C, Lu Z, Wang Y, et al. SPDET: edge-aware self-supervised panoramic depth estimation transformer with spherical geometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(10): 12474-12489.
20. Popescu M C, Balas V E, Perescu-Popescu L, et al. Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 2009, 8(7): 579-588.
21. Gong H, Chen G, Wang R, et al. Multi-task learning for thyroid nodule segmentation with thyroid region prior//2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021: 257-261.
22. Pedraza L, Vargas C, Narváez F, et al. An open access thyroid ultrasound image database//10th International Symposium on Medical Information Processing and Analysis. SPIE, 2015, 9287: 188-193.
23. 陈英, 张伟, 林洪平, 等. 医学图像分割算法的损失函数综述. 生物医学工程学杂志, 2023, 40(2): 392-400.
24. Wei J, Wang S, Huang Q. F³Net: fusion, feedback and focus for salient object detection//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(7): 12321-12328.
25. Loshchilov I, Hutter F. Fixing weight decay regularization in Adam. arXiv preprint, 2017, arXiv: 1711.05101.
26. 曹玉红, 徐海, 刘荪傲, 等. 基于深度学习的医学影像分割研究综述. 计算机应用, 2021, 41(8): 2273-2287.
27. Ma X, Sun B, Liu W, et al. AMSeg: a novel adversarial architecture based multi-scale fusion framework for thyroid nodule segmentation. IEEE Access, 2023, 11: 72911-72924.
28. Chen J, Lu Y, Yu Q, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint, 2021, arXiv: 2102.04306.
29. Bi H, Cai C, Sun J, et al. BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation. Computer Methods and Programs in Biomedicine, 2023, 238: 107614.
30. Zhang C, Wei G, Qiu M. HCMUnet: a hybrid CNN and Mamba network for medical ultrasound image segmentation. Preprint, 2024, http://dx.doi.org/10.2139/ssrn.4920609.
31. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas. arXiv preprint, 2018, arXiv: 1804.03999.
32. Huang X, Deng Z, Li D, et al. MISSFormer: an effective medical image segmentation transformer. arXiv preprint 2021, arXiv: 2109.07162.

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