Abstract
Airway segmentation is a prerequisite for diagnosing and screening pulmonary diseases. While computer aided algorithms have achieved great success in various medical image segmentation tasks, it remains a challenge in keeping the continuity of airway branches due to the special tubular shape. Some existing airway-specific segmentation models introduce topological representations such as neighbor connectivity and centerline overlapping into deep models and some other methods proposed customized network modules or training strategies based on the characteristics of airways. In this paper, we propose a large-kernel attention block to enlarge the receptive field as well as maintain the details of thin branches. We reformulate the segmentation problem into pixel-wise segmentation and connectivity prediction with a differentiable connectivity modeling technique, and also propose a self-correction loss to minimize the difference between these two tasks. In addition, the binary ground truth is transformed into distances from the boundary, and distance regression is used as additional supervision. Our proposed model has been evaluated on two public datasets, and the results show that our model outperforms other benchmark methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C.: ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote. Sens. 162, 94–114 (2020)
Gul, S., Khan, M.S., Bibi, A., Khandakar, A., Ayari, M.A., Chowdhury, M.E.: Deep learning techniques for liver and liver tumor segmentation: a review. Comput. Biol. Med. 105620 (2022)
Khan, Z., Yahya, N., Alsaih, K., Al-Hiyali, M.I., Meriaudeau, F.: Recent automatic segmentation algorithms of MRI prostate regions: a review. IEEE Access 9, 97878–97905 (2021)
Lo, P., et al.: Extraction of airways from CT (EXACT’09). IEEE Trans. Med. Imaging 31(11), 2093–2107 (2012)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Nan, Y., et al.: Fuzzy attention neural network to tackle discontinuity in airway segmentation. arXiv preprint arXiv:2209.02048 (2022)
Qin, Y., et al.: AirwayNet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 212–220. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_24
Qin, Y., Gu, Y., Zheng, H., Chen, M., Yang, J., Zhu, Y.M.: AirwayNet-SE: a simple-yet-effective approach to improve airway segmentation using context scale fusion. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 809–813. IEEE (2020)
Qin, Y., et al.: Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT. IEEE Trans. Med. Imaging 40(6), 1603–1617 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shit, S., et al.: clDice: a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560–16569 (2021)
Tetteh, G., et al.: DeepvesselNet: vessel segmentation, centerline prediction, and bifurcation detection in 3D angiographic volumes. Front. Neurosci. 1285 (2020)
Wang, C., et al.: Tubular structure segmentation using spatial fully connected network with radial distance loss for 3D medical images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 348–356. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_39
Yu, W., Zheng, H., Zhang, M., Zhang, H., Sun, J., Yang, J.: Break: bronchi reconstruction by geodesic transformation and skeleton embedding. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2022)
Zhang, M., Yang, G.Z., Gu, Y.: Differentiable topology-preserved distance transform for pulmonary airway segmentation. arXiv preprint arXiv:2209.08355 (2022)
Zhang, M., Zhang, H., Yang, G.Z., Gu, Y.: CFDA: collaborative feature disentanglement and augmentation for pulmonary airway tree modeling of COVID-19 CTs. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 506–516. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16431-6_48
Zhang, X., et al.: Progressive deep segmentation of coronary artery via hierarchical topology learning. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 391–400. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_38
Zheng, H.: BAS dataset download. https://github.com/haozheng-sjtu/3d-airway-segmentation
Zheng, H., et al.: Alleviating class-wise gradient imbalance for pulmonary airway segmentation. IEEE Trans. Med. Imaging 40(9), 2452–2462 (2021)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hu, Y., Meijering, E., Song, Y. (2024). Large-Kernel Attention Network with Distance Regression and Topological Self-correction for Airway Segmentation. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14471. Springer, Singapore. https://doi.org/10.1007/978-981-99-8388-9_10
Download citation
DOI: https://doi.org/10.1007/978-981-99-8388-9_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8387-2
Online ISBN: 978-981-99-8388-9
eBook Packages: Computer ScienceComputer Science (R0)