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Segmentation of the left atrial appendage based on fusion attention

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Abstract

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People’s Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms.

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References

  1. Al-Fahoum A (2003) Adaptive edge localisation approach for quantitative coronary analysis. Med Biol Eng Comput 41:425–31. https://doi.org/10.1007/BF02348085

    Article  CAS  PubMed  Google Scholar 

  2. Al-Fahoum A, Reza A (2004) Perceptually tuned jpeg coder for echocardiac image compression. IEEE Trans Inf Technol Biomed 8(3):313–320. https://doi.org/10.1109/TITB.2004.832545

    Article  PubMed  Google Scholar 

  3. Azad R, Aghdam EK, Rauland A, Jia Y, Avval AH, Bozorgpour A, Karimijafarbigloo S, Cohen JP, Adeli E, Merhof D (2022) Medical image segmentation review: the success of u-net. arXiv:2211.14830

  4. Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, Murrey B, Myronenko A, Zhao C, Yang D et al (2022) Monai: an open-source framework for deep learning in healthcare. arXiv:2211.02701

  5. Chen T, Ding S, Xie J, Yuan Y, Chen W, Yang Y, Ren Z, Wang Z (2019) Abd-net: attentive but diverse person re-identification. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 8351–8361

  6. Chen Z, He Z, Lu ZM (2024) DEA-Net: single image dehazing based on detail-enhanced convolution and content-guided attention. IEEE Trans Image Process 33:1002–1015. https://doi.org/10.1109/TIP.2024.3354108

    Article  PubMed  Google Scholar 

  7. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: medical image computing and computer-assisted intervention–MICCAI 2016: 19th international conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, pp 424–432

  8. Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3146–3154

  9. Gao S, Zhou H, Gao Y, Zhuang X (2023) BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability. Med Image Anal 89:102889. https://doi.org/10.1016/j.media.2023.102889

    Article  PubMed  Google Scholar 

  10. Grigoriadis GI, Sakellarios AI, Kosmidou I, Naka KK, Ellis C, Michalis LK, Fotiadis DI (2020) Wall shear stress alterations at left atrium and left atrial appendage employing abnormal blood velocity profiles. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 2565–2568. https://doi.org/10.1109/EMBC44109.2020.9175235

  11. Hassanin M, Anwar S, Radwan I, Khan FS, Mian A (2022) Visual attention methods in deep learning: an in-depth survey. arXiv:2204.07756

  12. Hesamian MH, Jia W, He X, Kennedy P (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32:582–596

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 13708–13717. https://doi.org/10.1109/CVPR46437.2021.01350

  14. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  15. Jin C, Feng J, Wang L, Yu H, Liu J, Lu J, Zhou J (2018) Left atrial appendage segmentation using fully convolutional neural networks and modified three-dimensional conditional random fields. IEEE J Biomed Health Inform 22(6):1906–1916. https://doi.org/10.1109/JBHI.2018.2794552

    Article  PubMed  Google Scholar 

  16. Juhl KA, Paulsen RR, Dahl AB, Dahl VA, De Backer O, Kofoed KF, Camara O (2019) Guiding 3D U-nets with signed distance fields for creating 3D models from images. arXiv:1908.10579 (2019)

  17. Luo X, Zhuang X (2023) \(\cal{X} \)-metric: an N-dimensional information-theoretic framework for groupwise registration and deep combined computing. IEEE Trans Pattern Anal Mach Intell 45:9206–9224. https://doi.org/10.1109/TPAMI.2022.3225418

    Article  PubMed  Google Scholar 

  18. Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D (2021) Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell 44(7):3523–3542

    Google Scholar 

  19. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32

  20. Qin Z, Zhang P, Wu F, Li X (2021) Fcanet: frequency channel attention networks. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 783–792

  21. Reddy VY, Sievert H, Halperin J, Doshi SK, Buchbinder M, Neuzil P, Huber K, Whisenant B, Kar S, Swarup V et al (2014) Percutaneous left atrial appendage closure vs warfarin for atrial fibrillation: a randomized clinical trial. JAMA 312:1988–1998. https://doi.org/10.1001/jama.2014.15192

    Article  CAS  PubMed  Google Scholar 

  22. Ronneberger O, Fischer P, Brox T (2015) 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

  23. Wang L, Feng J, Jin C, Lu J, Zhou J (2017) Left atrial appendage segmentation based on ranking 2-D segmentation proposals. In: Statistical atlases and computational models of the heart. Imaging and modelling challenges: 7th international workshop, STACOM 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 7, pp 21–29

  24. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155

  25. Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794–7803

  26. Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19

  27. Wu F, Zhuang X (2023) Minimizing estimated risks on unlabeled data: a new formulation for semi-supervised medical image segmentation. IEEE Trans Pattern Anal Mach Intell 45:6021–6036. https://doi.org/10.1109/TPAMI.2022.3215186

    Article  PubMed  Google Scholar 

  28. Xie Q, Lai YK, Wu J, Wang Z, Zhang Y, Xu K, Wang J (2020) Mlcvnet: multi-level context votenet for 3d object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10447–10456

  29. Zagoruyko S, Komodakis N (2016) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv:1612.03928

  30. Zhao H, Jia J, Koltun V (2020) Exploring self-attention for image recognition. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 10073–10082. https://doi.org/10.1109/CVPR42600.2020.01009

  31. Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11):1668–1681. https://doi.org/10.1109/TMI.2008.2004421

    Article  PubMed  Google Scholar 

  32. Zheng Y, Liu S, Xu X, Zhao J, Wang H, Liang D, Yu T, Zhu Y (2021) Quantification of pectinate muscles inside left atrial appendage from CT images using fractal analysis. In: 2021 IEEE international conference on medical imaging physics and engineering (ICMIPE), pp 1–5. https://doi.org/10.1109/ICMIPE53131.2021.9698958

  33. Zheng Y, Yang D, John M, Comaniciu D (2013) Multi-part modeling and segmentation of left atrium in C-arm CT for image-guided ablation of atrial fibrillation. IEEE Trans Med imaging 33(2):318–331

    Article  Google Scholar 

  34. Zhuang X (2019) Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans Pattern Anal Mach Intell 41:2933–2946. https://doi.org/10.1109/TPAMI.2018.2869576

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61971118, No. 61373088, No. 61402298), the National Natural Science Foundation of Liaoning (No. LJKMZ20220523), and the Aviation Science Foundation (2019ZE054009).

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Authors and Affiliations

  1. School of Computer Sciences, Shenyang Aerospace University, Shenyang, China

    Guodong Zhang, Kaichao Liang, Yanlin Li, Tingyu Liang & Zhaoxuan Gong

  2. Department of Radiology, People’s Hospital of Liaoning Province, Shenyang, China

    Ronghui Ju

  3. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China

    Guodong Zhang & Dazhe Zhao

  4. Luyuan Hydropower Company Maintenance Company, State Grid, Dandong, China

    Zhuoning Zhang

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  1. Guodong Zhang
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Correspondence to Guodong Zhang or Ronghui Ju.

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The subject was reviewed by the Ethics Committee of the People’s Hospital of Liaoning Province, Grant No. (2022) H001.

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Zhang, G., Liang, K., Li, Y. et al. Segmentation of the left atrial appendage based on fusion attention. Med Biol Eng Comput 62, 2999–3012 (2024). https://doi.org/10.1007/s11517-024-03104-0

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