Abstract
Brain midline delineates the boundary between the two cerebral hemispheres of the human brain, which plays a significant role in guiding intracranial hemorrhage surgery. Large midline shift caused by hematomas remains an inherent challenge for delineation. However, most previous methods only handle normal brains and delineate the brain midline on 2D CT images. In this study, we propose a novel hemisphere-segmented framework (HSF) for generating smooth 3D brain midline especially when large hematoma shifts the midline. Our work has four highlights. First, we propose to formulate the brain midline delineation as a 3D hemisphere segmentation task, which recognizes the midline location via enriched anatomical features. Second, we employ a distance-weighted map for midline aware loss. Third, we introduce rectificative learning for the model to handle various head poses. Finally, considering the complexity of hematomas distribution in human brain, we build a classification model to automatically identify the situation when hematoma breaks into brain ventricles and formulate a midline correction strategy to locally adjust the midline according to the location and boundary of hematomas. To our best knowledge, it is the first study focusing on delineating the brain midline on 3D CT images of hematoma patients and handling the situation of ventricle break-in. Through extensive validations on a large in-house dataset, our method outperforms state-of-the-art methods in various evaluation metrics.
C. Qin and H. Li—The two authors contribute equally to this work.
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References
Caceres, J.A., Goldstein, J.N.: Intracranial hemorrhage. Emerg. Med. Clin. North Am. 30(3), 771 (2012)
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Liao, C.C., Xiao, F., Wong, J.M., Chiang, I.J.: Automatic recognition of midline shift on brain CT images. Comput. Biol. Med. 40(3), 331–339 (2010)
Liu, R., et al.: Automatic detection and quantification of brain midline shift using anatomical marker model. Comput. Med. Imaging Graph. 38(1), 1–14 (2014)
Pisov, M., et al.: Incorporating task-specific structural knowledge into CNNs for brain midline shift detection. In: Suzuki, K., et al. (eds.) ML-CDS/IMIMIC -2019. LNCS, vol. 11797, pp. 30–38. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33850-3_4
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
Rundo, L., et al.: USE-NET: incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365, 31–43 (2019)
Wang, S., Liang, K., Li, Y., Yu, Y., Wang, Y.: Context-aware refinement network incorporating structural connectivity prior for brain midline delineation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 208–217. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_21
Wang, W.Z., et al.: Minimally invasive craniopuncture therapy vs. conservative treatment for spontaneous intracerebral hemorrhage: results from a randomized clinical trial in china. Int. J. Stroke 4(1), 11–16 (2009)
Wei, H., et al.: Regression-based line detection network for delineation of largely deformed brain midline. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 839–847. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_93
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Qin, C. et al. (2021). 3D Brain Midline Delineation for Hematoma Patients. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_49
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DOI: https://doi.org/10.1007/978-3-030-87240-3_49
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