Abstract
Convolutions neural networks have obtained promising results in various medical image segmentation tasks. However, these methods ignore the problem of domain shift, which will lead to a model trained in a source domain performing poorly when applied to different target domains. In this work, we propose a two-stage segmentation network, and utilize histogram matching to eliminate domain shift. Specifically, the first stage obtains the region of interest by performing coarsely segmentation on down-sample images. Then the second stage segments the left atrium (LA) based on the region of interest. The method is evaluated on LAScarQS 2022 data-set, acquiring average Dice of 0.87790 for LA segmentation. Besides, the two-stage network is about four times faster against a single-stage network in the test phase.
X. Zhang and X. Yang—The two authors have equal contributions to the paper.
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References
Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_54
Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605–613. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_67
Habijan, M., et al.: Overview of the whole heart and heart chamber segmentation methods. Cardiovasc. Eng. Technol. 11(6), 725–747 (2020)
Xia, Q., Yao, Y., Hu, Z., Hao, A.: Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 211–220. Springer, Automatic 3d atrial segmentation from ge-mris using volumetric fully convolutional networks (2019). https://doi.org/10.1007/978-3-030-12029-0_23
Chen, C., Biffi, C., Tarroni, G., Petersen, S., Bai, W., Rueckert, D.: Learning shape priors for robust cardiac MR segmentation from multi-view images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 523–531. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_58
Chen, C., Dou, Q., Jin, Y., Chen, H., Qin, J., Heng, P.-A.: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 447–456. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_50
Dou, D., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. Advances in Neural Information Processing Systems, 32 (2019)
Ma, J.: Histogram matching augmentation for domain adaptation with application to multi-centre, multi-vendor and multi-disease cardiac image segmentation. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 177–186. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68107-4_18
Shapira, D., Avidan, S., Hel-Or, Y.: Multiple histogram matching. In: 2013 IEEE international conference on image processing, pp. 2269–2273. IEEE (2013)
Isensee, F., et al.: nnu-net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)
Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: Atrialjsqnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med. Image Anal. 76, 102303 (2022)
Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: Medical image analysis on left atrial lge mri for atrial fibrillation studies: a review. Med. Image Anal., p. 102360 (2022)
Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 557–566. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_54
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Zhang, X., Yang, X., Huang, L., Huang, L. (2023). Two Stage of Histogram Matching Augmentation for Domain Generalization: Application to Left Atrial Segmentation. In: Zhuang, X., Li, L., Wang, S., Wu, F. (eds) Left Atrial and Scar Quantification and Segmentation. LAScarQS 2022. Lecture Notes in Computer Science, vol 13586. Springer, Cham. https://doi.org/10.1007/978-3-031-31778-1_6
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