Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment outcomes in AF. In this paper, we proposed to automatically segment LA cavity and quantify LA scars with late gadolinium enhancement Magnetic Resonance Imagings (LGE-MRIs). We adopted nnU-Net as the baseline model and exploited the importance of LA boundary characteristics with the TopK loss as the loss function. Specifically, a focus on LA boundary pixels is achieved during training, which provides a more accurate boundary prediction. On the other hand, a distance map transformation of the predicted LA boundary is regarded as an additional input for the LA scar prediction, which provides marginal constraint on scar locations. We further designed a novel uncertainty-aware module (UAM) to produce better results for predictions with high uncertainty. Experiments on the LAScarQS 2022 dataset demonstrated our model’s superior performance on the LA cavity and LA scar segmentation. Specifically, we achieved 88.98% and 64.08% Dice coefficient for LA cavity and scar segmentation, respectively. We will make our implementation code public available at https://github.com/level6626/Boundary-focused-nnU-Net.
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References
Alom, M.Z., Yakopcic, C., Hasan, M., Taha, T.M., Asari, V.K.: Recurrent residual U-Net for medical image segmentation. J. Med. Imaging 6(1), 014006 (2019)
Cheng, B., Girshick, R., Dollár, P., Berg, A.C., Kirillov, A.: Boundary IoU: improving object-centric image segmentation evaluation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15334–15342 (2021)
Chung, M.K., Eckhardt, L.L., Chen, L.Y., Ahmed, H.M., Gopinathannair, R., Joglar, J.A., Noseworthy, P.A., Pack, Q.R., Sanders, P., Trulock, K.M., et al.: Lifestyle and risk factor modification for reduction of atrial fibrillation: a scientific statement from the American heart association. Circulation 141(16), e750–e772 (2020)
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J.M.R.S., Belagiannis, V., Papa, J.P., Nascimento, J.C., Loog, M., Lu, Z., Cardoso, J.S., Cornebise, J. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 179–187. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_19
Isensee, F., et al.: nnu-net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)
Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 285–296. PMLR (2019)
Li, L., Wu, F., Yang, G., Xu, L., Wong, T., Mohiaddin, R., Firmin, D., Keegan, J., Zhuang, X.: Atrial scar quantification via multi-scale CNN in the graph-cuts framework. Med. Image Anal. 60, 101595 (2020)
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., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 557–566. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_54
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. Medical Image Analysis, p. 102360 (2022)
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., Martel, A.L.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)
Meng, Y., et al.: Shape-aware weakly/semi-supervised optic disc and cup segmentation with regional/marginal consistency. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13434. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16440-8_50
Meng, Y., Meng, W., Gao, D., Zhao, Y., Yang, X., Huang, X., Zheng, Y.: Regression of instance boundary by aggregated CNN and GCN. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 190–207. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_12
Meng, Y., Wei, M., Gao, D., Zhao, Y., Yang, X., Huang, X., Zheng, Y.: CNN-GCN aggregation enabled boundary regression for biomedical image segmentation. 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. 12264, pp. 352–362. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_35
Meng, Y., et al.: BI-GCN: boundary-aware input-dependent graph convolution network for biomedical image segmentation. In: 32nd British Machine Vision Conference: BMVC 2021. British Machine Vision Association (2021)
Meng, Y., et al.: Dual consistency enabled weakly and semi-supervised optic disc and cup segmentation with dual adaptive graph convolutional networks. IEEE Trans. Med. Imaging in press (2022)
Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qian, X., Huang, X., Zheng, Y.: Spatial uncertainty-aware semi-supervised crowd counting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15549–15559 (2021)
Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qiao, Y., MacCormick, I.J., Huang, X., Zheng, Y.: Graph-based region and boundary aggregation for biomedical image segmentation. IEEE Trans. Med. Imaging 41(3), 690–701 (2021)
Oktay, O., et al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Ranjan, R., et al.: Gaps in the ablation line as a potential cause of recovery from electrical isolation and their visualization using MRI. Circulation: Arrhythmia Electrophysiology 4(3), 279–286 (2011)
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
Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Comput. Commun. Rev. 5(1), 3–55 (2001)
Siebermair, J., Kholmovski, E.G., Marrouche, N.: Assessment of left atrial fibrosis by late gadolinium enhancement magnetic resonance imaging: methodology and clinical implications. JACC: Clin. Electrophysiology 3(8), 791–802 (2017)
Wu, Z., Shen, C., Hengel, A.v.d.: Bridging category-level and instance-level semantic image segmentation. arXiv preprint arXiv:1605.06885 (2016)
Yang, G., Chen, J., Gao, Z., Li, S., Ni, H., Angelini, E., Wong, T., Mohiaddin, R., Nyktari, E., Wage, R., et al.: Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. Futur. Gener. Comput. Syst. 107, 215–228 (2020)
Yang, G., et al.: A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced mri images. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 844–848. IEEE (2017)
Yang, X., Wang, N., Wang, Y., Wang, X., Nezafat, R., Ni, D., Heng, P.-A.: Combating uncertainty with novel losses for automatic left atrium segmentation. In: Pop, M., Sermesant, M., Zhao, J., Li, S., McLeod, K., Young, A., Rhode, K., Mansi, T. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 246–254. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_27
Zhao, Z., Puybareau, E., Boutry, N., Géraud, T.: Do not treat boundaries and regions differently: An example on heart left atrial segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7447–7453. IEEE (2021)
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Zhang, Y., Meng, Y., Zheng, Y. (2023). Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-Focused nnU-Net. 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_5
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