Abstract
To alleviate the demand for a large amount of annotated data by deep learning methods, this paper explores self-supervised learning (SSL) for brain structure segmentation. Most SSL methods treat all pixels equally, failing to emphasize the boundaries that are important clues for segmentation. We propose Boundary-Enhanced Self-Supervised Learning (BE-SSL), leveraging supervoxel segmentation and registration as two related proxy tasks. The former task enables capture boundary information by reconstructing distance transform map transformed from supervoxels. The latter task further enhances the boundary with semantics by aligning tissues and organs in registration. Experiments on CANDI and LPBA40 datasets have demonstrated that our method outperforms current SOTA methods by 0.89% and 0.47%, respectively. Our code is available at https://github.com/changfeng3168/BE-SSL.
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
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019)
Chen, X., He, K.: Exploring simple Siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)
Feng, R., Zhou, Z., Gotway, M.B., Liang, J.: Parts2Whole: self-supervised contrastive learning via reconstruction. In: Albarqouni, S., et al. (eds.) DART/DCL -2020. LNCS, vol. 12444, pp. 85–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60548-3_9
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)
Jaderberg, M., et al.: Spatial transformer networks. Adv. Neural Inf. Process. Syst. 28, 1–9 (2015)
Jena, R., Singla, S., Batmanghelich, K.: Self-supervised vessel enhancement using flow-based consistencies. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 242–251. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_23
Jin, Y., Buntine, W., Petitjean, F., Webb, G.I.: Discriminative, generative and self-supervised approaches for target-agnostic learning. arXiv preprint arXiv:2011.06428 (2020)
Kennedy, D.N., Haselgrove, C., Hodge, S.M., Rane, P.S., Makris, N., Frazier, J.A.: CANDIShare: a resource for pediatric neuroimaging data (2012)
Ma, J., et al.: How distance transform maps boost segmentation CNNs: an empirical study. In: Medical Imaging with Deep Learning, pp. 479–492. PMLR (2020)
Shattuck, D.W., et al.: Construction of a 3d probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064–1080 (2008)
Taleb, A., et al.: 3d self-supervised methods for medical imaging. Adv. Neural Inf. Process. Syst. 33, 18158–18172 (2020)
Wang, S., et al.: LT-Net: label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9162–9171 (2020)
Zhang, L., Wang, M., Liu, M., Zhang, D.: A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci. 14, 779 (2020)
Zhang, X., Feng, S., Zhou, Y., Zhang, Y., Wang, Y.: SAR: scale-aware restoration learning for 3D tumor segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 124–133. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_12
Zhou, H.Y., Lu, C., Yang, S., Han, X., Yu, Y.: Preservational learning improves self-supervised medical image models by reconstructing diverse contexts. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3499–3509 (2021)
Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. 67, 101840 (2021)
Zhuang, X., Li, Y., Hu, Y., Ma, K., Yang, Y., Zheng, Y.: self-supervised feature learning for 3D medical images by playing a Rubik’s cube. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 420–428. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_46
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chang, F., Wu, C., Wang, Y., Zhang, Y., Chen, X., Tian, Q. (2022). Boundary-Enhanced Self-supervised Learning for Brain Structure Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-16431-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16430-9
Online ISBN: 978-3-031-16431-6
eBook Packages: Computer ScienceComputer Science (R0)
