Abstract
Medical image segmentation is the premise of many medical image applications including disease diagnosis, anatomy, and radiation therapy. This paper presents a k-Dense-UNet for segmentation of Electron Microscopy (EM) images. Firstly, based on the characteristics of the long skip connection of U-Net and the mechanism of short skip connection of DenseNet, we propose a Dense-UNet by embedding the dense blocks into U-Net, leading to deeper layers for better feature extraction. We experimentally show that Dense-UNet outperforms the popular U-Net. Secondly, we combine Dense-UNet with one of the newest U-Net variants called kU-Net into a network called k-Dense-UNet, which consists of multiple Dense-UNet submodules. Skip connections are added between the adjacent submodules, to pass information efficiently, helping the model to identify fine features. Experimental results on the ISBI 2012 EM dataset show that k-Dense-UNet achieves better performance than U-Net and some of its variants.
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
Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NIPS (2012)
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
Quan, T.M., Hildebrand, D.G.C., Jeong, W.-K.: FusioNnet: a deep fully residual convolutional neural network for image segmentation in connectomics, CoRR, vol. abs/1612.05360 (2016)
Chen, H., Qi, X., Cheng, J.-Z., Heng, P.-A.: Deep contextual networks for neuronal structure segmentation. In: AAAI (2016)
Chen, K., Zhu, D., Lu, J., Luo, Y.: An adversarial and densely dilated network for connectomes segmentation. Symmetry 10, 467 (2018)
Drozdzal, M., et al.: Learning normalized inputs for iterative estimation in medical image segmentation. Med. Image Anal. 44, 1–13 (2018)
Chen, J., Yang, L., Zhang, Y., Alber, M., Chen, D.Z.: Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation. In: NIPS (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. TPAMI (2017)
Shen, W., Wang, B., Jiang, Y., Wang, Y., Yuille, A.L.: Multi-stage multi-recursive-input fully convolutional networks for neuronal boundary detection. In: ICCV (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)
Arganda-Carreras, I., et al.: Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9, 142 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Xu, L.: Least MSE reconstruction for self-organization: (i)&(ii). In: Proceedings of 1991 International Joint Conference on Neural Networks, pp. 2363–2373 (1991)
Xu, L.: Least mean square error reconstruction principle for self-organizing neural-nets. Neural Netw. 6(5), 627–648 (1993)
Xu, L.: An overview and perspectives on bidirectional intelligence: Lmser duality, double IA harmony, and causal computation. IEEE/CAA Journal of Automatica Sinica 6(4), 865–893 (2019)
Acknowledgement
The first author would like to thank Yuze Guo for helpful discussions. This work was supported by the Zhi-Yuan Chair Professorship Start-up Grant (WF220103010), and Startup Fund (WF220403029) for Youngman Research, from Shanghai Jiao Tong University.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Qiang, Z., Tu, S., Xu, L. (2019). A k-Dense-UNet for Biomedical Image Segmentation. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-36189-1_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36188-4
Online ISBN: 978-3-030-36189-1
eBook Packages: Computer ScienceComputer Science (R0)