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HDNeXt: Hybrid Dynamic MedNeXt with Level Set Regularization for Medical Image Segmentation

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Computer Vision – ACCV 2024 (ACCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15478))

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Abstract

Deep learning has been extensively employed in the field of medical image segmentation, demonstrating its robustness and efficacy. However, the pursuit of consistent segmentation performance across diverse instrumental conditions and the challenge of achieving precise boundary delineation in segmented images remain significant hurdles. In this paper, we aim to develop a model capable of achieving consistent, high-quality segmentation of identical regions of interest across varying instrumental conditions, with precise boundary delineation. Toward this end, we introduce our Hybrid Dynamic MedNeXt (HDNeXt) model, an advanced framework capable of dynamically generating weights across diverse medical images to maintain consistently high segmentation performance. HDNeXt builds on the robust segmentation framework of MedNeXt by incorporating dynamic convolution techniques, which endow the model with the capability for dynamic weight adjustment, significantly enhancing its segmentation performance. To tackle the second challenge, we devised a novel loss function, \(L_{CR}\), formulated on the Curvature of the segmentation boundary and Region-Fitting energy derived from level set methods, which significantly enhances boundary precision during training and optimizes overall segmentation performance. Experiments were conducted on the abdominal CT datasets Synapse and the cardiac MRI datasets ACDC to demonstrate the efficiency and effectiveness of our method. Our method achieved an average Dice coefficient of 84.38 on the Synapse datasets and 93.59 on the ACDC datasets, surpassing other 2D state-of-the-art segmentation models and achieving optimal performance for 2D medical image segmentation. Codes are available at https://github.com/HaoyuCao/HDNeXt.

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References

  1. Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision. pp. 205–218. Springer (2022)

    Google Scholar 

  2. Chambolle, A., Pock, T.: Total roto-translational variation. Numer. Math. 142, 611–666 (2019)

    Article  MathSciNet  Google Scholar 

  3. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  Google Scholar 

  4. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. ArXiv preprint arXiv:2102.04306 (2021)

  5. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  6. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11030–11039 (2020)

    Google Scholar 

  7. Dai, Z., Liu, H., Le, Q.V., Tan, M.: Coatnet: Marrying convolution and attention for all data sizes. Adv. Neural. Inf. Process. Syst. 34, 3965–3977 (2021)

    Google Scholar 

  8. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv preprint arXiv:2010.11929 (2020)

  9. Ha, D.T., Phuong, D.L.: Freedom of information law comes to vietnam: How do human rights adapt to goals of economic development and political stability? Austl. J. Asian L. 18, 167 (2017)

    Google Scholar 

  10. Han, Q., Fan, Z., Dai, Q., Sun, L., Cheng, M.M., Liu, J., Wang, J.: On the connection between local attention and dynamic depth-wise convolution. ArXiv preprint arXiv:2106.04263 (2021)

  11. Huang, T., Huang, L., You, S., Wang, F., Qian, C., Xu, C.: Lightvit: Towards light-weight convolution-free vision transformers. arXiv preprint arXiv:2207.05557 (2022)

  12. Huang, X., Deng, Z., Li, D., Yuan, X.: Missformer: An effective medical image segmentation transformer. arXiv preprint arXiv:2109.07162 (2021)

  13. Huang, Z., Zhang, Z., Lan, C., Zha, Z.J., Lu, Y., Guo, B.: Adaptive frequency filters as efficient global token mixers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6049–6059 (2023)

    Google Scholar 

  14. Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Article  Google Scholar 

  15. Kim, B., Ye, J.C.: Mumford-shah loss functional for image segmentation with deep learning. IEEE Trans. Image Process. 29, 1856–1866 (2019)

    Article  MathSciNet  Google Scholar 

  16. Kim, Y., Kim, S., Kim, T., Kim, C.: Cnn-based semantic segmentation using level set loss. In: 2019 IEEE winter conference on applications of computer vision (WACV). pp. 1752–1760. IEEE (2019)

    Google Scholar 

  17. Kuiper, N.H.: Minimal total absolute curvature for immersions. Invent. Math. 10(3), 209–238 (1970)

    Article  MathSciNet  Google Scholar 

  18. Langer, J., Singer, D.A.: The total squared curvature of closed curves. Journal of Differential Geometry 20(1), 1–22 (1984)

    Article  MathSciNet  Google Scholar 

  19. Li, C., Zhou, A., Yao, A.: Omni-dimensional dynamic convolution. ArXiv preprint arXiv:2209.07947 (2022)

  20. Li, C., Gore, J.C., Davatzikos, C.: Multiplicative intrinsic component optimization (mico) for mri bias field estimation and tissue segmentation. Magn. Reson. Imaging 32(7), 913–923 (2014)

    Article  Google Scholar 

  21. Li, C., Kao, C.Y., Gore, J.C., Ding, Z.: Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)

    Article  MathSciNet  Google Scholar 

  22. Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., Fei-Fei, L.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 82–92 (2019)

    Google Scholar 

  23. Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al.: Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12009–12019 (2022)

    Google Scholar 

  24. 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)

    Google Scholar 

  25. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11976–11986 (2022)

    Google Scholar 

  26. Ma, D., Liao, Q., Chen, Z., Liao, R., Ma, H.: Adaptive local-fitting-based active contour model for medical image segmentation. Signal Processing: Image Communication 76, 201–213 (2019)

    Google Scholar 

  27. Niu, S., Chen, Q., De Sisternes, L., Ji, Z., Zhou, Z., Rubin, D.L.: Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn. 61, 104–119 (2017)

    Article  Google Scholar 

  28. Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large kernel matters–improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4353–4361 (2017)

    Google Scholar 

  29. Rahman, M.M., Marculescu, R.: Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 6222–6231 (2023)

    Google Scholar 

  30. Rahman, M.M., Marculescu, R.: G-cascade: Efficient cascaded graph convolutional decoding for 2d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 7728–7737 (2024)

    Google Scholar 

  31. Rahman, M.M., Marculescu, R.: Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image segmentation. In: Medical Imaging with Deep Learning. pp. 1526–1544. PMLR (2024)

    Google Scholar 

  32. Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J.B., Larochelle, H., Zemel, R.S.: Meta-learning for semi-supervised few-shot classification. ArXiv preprint arXiv:1803.00676 (2018)

  33. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)

    Google Scholar 

  34. Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., Petersen, J., Isensee, F., Jaeger, P.F., Maier-Hein, K.H.: Mednext: transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 405–415. Springer (2023)

    Google Scholar 

  35. Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 843–852 (2017)

    Google Scholar 

  36. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  37. Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., Xie, S.: Convnext v2: Co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16133–16142 (2023)

    Google Scholar 

  38. Wu, J., Ji, W., Fu, H., Xu, M., Jin, Y., Xu, Y.: Medsegdiff-v2: Diffusion-based medical image segmentation with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 6030–6038 (2024)

    Google Scholar 

  39. Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9653–9663 (2022)

    Google Scholar 

  40. Xu, K., Qin, M., Sun, F., Wang, Y., Chen, Y.K., Ren, F.: Learning in the frequency domain. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1740–1749 (2020)

    Google Scholar 

  41. Yang, B., Bender, G., Le, Q.V., Ngiam, J.: Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems 32 (2019)

    Google Scholar 

  42. Yang, Y., Yan, T., Jiang, X., Xie, R., Li, C., Zhou, T.: Mh-net: Model-data-driven hybrid-fusion network for medical image segmentation. Knowl.-Based Syst. 248, 108795 (2022)

    Article  Google Scholar 

  43. Yu, W., Zhou, P., Yan, S., Wang, X.: Inceptionnext: When inception meets convnext. arXiv preprint arXiv:2303.16900 (2023)

  44. Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: nnformer: Interleaved transformer for volumetric segmentation. ArXiv preprint arXiv:2109.03201 (2021)

  45. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)

    Article  Google Scholar 

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Acknowledgments

This research is supported by National Natural Science Foundation of China No. 62371156 and Natural Science Foundation of Guangdong Province No. 2022A1515011629, University Innovative Team Project of Guangdong 2022KCXTD0 39, and National Natural Science Foundation of China (12371419).

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Authors and Affiliations

  1. School of Science, Harbin Institute of Technology, Shenzhen, China

    Haoyu Cao, Tianyi Han & Yunyun Yang

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  1. Haoyu Cao
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  2. Tianyi Han
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  3. Yunyun Yang
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Correspondence to Yunyun Yang .

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Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

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Cao, H., Han, T., Yang, Y. (2025). HDNeXt: Hybrid Dynamic MedNeXt with Level Set Regularization for Medical Image Segmentation. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15478. Springer, Singapore. https://doi.org/10.1007/978-981-96-0963-5_24

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  • DOI: https://doi.org/10.1007/978-981-96-0963-5_24

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