Abstract
Deep convolutional neural networks have shown great potential in medical image segmentation. However, automatic cardiac segmentation is still challenging due to the heterogeneous intensity distributions and indistinct boundaries in cardiac magnetic resonance (CMR) images, especially for myocardial pathology segmentation. In this paper, we present a dual-path feature aggregation network combined multi-layer fusion (MF&DFA-Net) to overcome these misclassification and shape discontinuity problems in myocardial pathology segmentation. The proposed network is aimed to maintain a realistic shape of the segmentation results and predict the position of myocardial pathology, which network is divided into two parts: the first part is a non-downsampling multiscale nested network (MN-Net) which restrains the cardiac shape and maintains the global information, and the second part is multiscale symmetric encoding and decoding network (MSED-Net) that can retain details. Three sequences of CMR images were adopted for multi-layer fusion training which included three inputs and one output. We can segment left ventricular (LV) blood pool, right ventricular (RV) blood pool, LV normal myocardium, LV myocardial edema, LV myocardial scars simultaneously with MF&DFA-Net. We randomly took the 90% data for training and 10% data for verification which data provided by the organizer of the 2020 Medical Image Computing and Computer Assisted Interventions (MICCAI) myocardial pathology segmentation challenge (MyoPS 2020). Compared with inter-observer, we increased the Dice value of myocardial scar segmentation by 8.08%.
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
Petitjean, C., Dacher, J.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169–184 (2011)
Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384–395 (2018)
Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933–2946 (2019)
Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 581–588. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_67
Chen, C., et al.: Unsupervised multi-modal style transfer for cardiac MR segmentation. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 209–219. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39074-7_22
Campello, V.M., MartÃn-Isla, C., Izquierdo, C., Petersen, S.E., Ballester, M.A.G., Lekadir, K.: Combining multi-sequence and synthetic images for improved segmentation of late gadolinium enhancement cardiac MRI. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 290–299. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39074-7_31
Wang, X., et al.: SK-Unet: an improved U-Net model with selective kernel for the segmentation of multi-sequence cardiac MR. In: Pop, M., Sermesant, M., Camara, O., Zhuang, X., Li, S., Young, A., Mansi, T., Suinesiaputra, A. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 246–253. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39074-7_26
Wang, J., Huang, H., Chen, C., Ma, W., Huang, Y., Ding, X.: Multi-sequence cardiac MR segmentation with adversarial domain adaptation network. In: Pop, M., Sermesant, M., Camara, O., Zhuang, X., Li, S., Young, A., Mansi, T., Suinesiaputra, A. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 254–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39074-7_27
Li, J., Yu, Z., Gu, Z., Liu, H., Li, Y.: Dilated-Inception Net: multi-Scale feature aggregation for cardiac right ventricle segmentation. IEEE Trans. Bio-Med. Eng. 66(12), 3499–3508 (2019)
Khened, M., Alex, V., Krishnamurthi, G.: Fully convolutional multi-scale residual DenseNet-s for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21–45 (2018)
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
Jégou, S., Drozdzal, M., Vázquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1175–1183 (2017)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Acknowledgements
This work was supported by the National Natural Science Foundation of China [Nos. 61972060 and U1713213], National Science & Technology Major Project [2016YFC1000307-3], Natural Science Foundation of Chongqing [cstc2019cxcyljrc-td0270, cstc2019jcyj-cxttX0002, cstc2019jcyj-zdxmX0011].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, F., Li, W. (2020). Dual-Path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR. In: Zhuang, X., Li, L. (eds) Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images. MyoPS 2020. Lecture Notes in Computer Science(), vol 12554. Springer, Cham. https://doi.org/10.1007/978-3-030-65651-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-65651-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65650-8
Online ISBN: 978-3-030-65651-5
eBook Packages: Computer ScienceComputer Science (R0)