Abstract
In the clinical environment, myocardial infarction (MI) as one common cardiovascular disease is mainly evaluated based on the late gadolinium enhancement (LGE) cardiac magnetic resonance images (CMRIs). The automatic segmentations of left ventricle (LV), right ventricle (RV), and left ventricular myocardium (LVM) in the LGE CMRIs are desired for the aided diagnosis in clinic. To accomplish this segmentation task, this paper proposes a modified U-net architecture by combining multi-sequence CMRIs, including the cine, LGE, and T2-weighted CMRIs. The cine and T2-weighted CMRIs are used to assist the segmentation in the LGE CMRIs. In this segmentation network, the squeeze-and-excitation residual (SE-Res) and selective kernel (SK) modules are inserted in the down-sampling and up-sampling stages, respectively. The SK module makes the obtained feature maps more informative in both spatial and channel-wise space, and attains more precise segmentation result. The utilized dataset is from the MICCAI challenge (MS-CMRSeg 2019), which is acquired from 45 patients including three CMR sequences. The cine and T2-weighted CMRIs acquired from 35 patients and the LGE CMRIs acquired from 5 patients are labeled. Our method achieves the mean dice score of 0.922 (LV), 0.827 (LVM), and 0.874 (RV) in the LGE CMRIs.
X. Wang and S. Yang—Co-first authors.
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
Similar content being viewed by others
References
Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. (2018). https://doi.org/10.1109/TPAMI.2018.2869576
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
Khened, M., Kollerathu, V.A., Krishnamurthi, G.: Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21–45 (2019)
Guo, F., Ng, M., Wright, G.: Cardiac MRI left ventricle segmentation and quantification: a framework combining U-Net and continuous max-flow. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 450–458. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_48
Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 120–129. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_13
Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual U-Net. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 371–380. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_40
Li, J., Hu, Z.: Left ventricle full quantification using deep layer aggregation based multitask relationship learning. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 381–388. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_41
Zheng, Q., Delingette, H., Duchateau, N., Ayache, N.: 3-D consistent and robust segmentation of cardiac images by deep learning with spatial propagation. IEEE Trans. Med. Imaging 37(9), 2137–2148 (2018)
Ciofolo, C., Fradkin, M., Mory, B., Hautvast, G., Breeuwer, M.: Automatic myocardium segmentation in late-enhancement MRI. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 225–228. IEEE, Piscataway (2008)
Dikici, E., O’Donnell, T., Setser, R., White, R.D.: Quantification of delayed enhancement MR images. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 250–257. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30135-6_31
El Berbari, R., Kachenoura, N., Frouin, F., Herment, A., Mousseaux, E., Bloch, I.: An automated quantification of the transmural myocardial infarct extent using cardiac DE-MR images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4403–4406. IEEE, Piscataway (2009)
Rajchl, M., Jing, Y., White, J.A., Ukwatta, E., Stirrat, J., Nambakhsh, C.M.S., et al.: Interactive hierarchical-flow segmentation of scar tissue from late-enhancement cardiac MR images. IEEE Trans. Med. Imaging 33(1), 159–172 (2014)
Wei, D., Sun, Y., Ong, S.H., Chai, P., Teo, L.L., Low, A.F.: Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long-and short-axis information. Med. Image Anal. 17(6), 685–697 (2013)
Xu, R.S., Athavale, P., Lu, Y., Radau, P., Wright, G.A.: Myocardial segmentation in late-enhancement MR images via registration and propagation of cine contours. In: 10th International Symposium on Biomedical Imaging, pp. 844–847. IEEE, Piscataway (2013)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE Conference on CVPR, pp. 7132–7141, IEEE, Piscataway (2018)
Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. arXiv:1903.06586 (2019)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing, pp. 1–4, IEEE, Piscataway (2017)
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
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X. et al. (2020). SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-39074-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39073-0
Online ISBN: 978-3-030-39074-7
eBook Packages: Computer ScienceComputer Science (R0)
