Abstract
Despite some design limitations, CNNs have been largely adopted by the computer vision community due to their efficacy and versatility. Introduced by Sabour et al. to circumvent some limitations of CNNs, capsules replace scalars with vectors to encode appearance feature representation, allowing better preservation of spatial relationships between whole objects and its parts. They also introduced the dynamic routing mechanism, which allows to weight the contributions of parts to a whole object differently at each inference step. Recently, Hinton et al. have proposed to solely encode pose information to model such part-whole relationships. Additionally, they used a matrix instead of a vector encoding in the capsules framework. In this work, we introduce several improvements to the capsules framework, allowing it to be applied for multi-label semantic segmentation. More specifically, we combine pose and appearance information encoded as matrices into a new type of capsule, i.e. Matwo-Caps. Additionally, we propose a novel routing mechanism, i.e. Dual Routing, which effectively combines these two kinds of information. We evaluate our resulting Matwo-CapsNet on the JSRT chest X-ray dataset by comparing it to SegCaps, a capsule based network for binary segmentation, as well as to other CNN based state-of-the-art segmentation methods, where we show that our Matwo-CapsNet achieves competitive results, while requiring only a fraction of the parameters of other previously proposed methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Our code is available at https://github.com/savinienb/Matwo-CapsNet.
- 2.
References
van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med. Image Anal. 10(1), 19–40 (2006)
Hinton, G.E., Krizhevsky, A., Wang, S.D.: Transforming auto-encoders. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 44–51. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21735-7_6
Hinton, G.E., Sabour, S., Frosst, N.: Matrix capsules with EM routing. In: International Conference on Learning Representations (ICLR) (2018)
LaLonde, R., Bagci, U.: Capsules for Object Segmentation. In: International Conference on Medical Imaging with Deep Learning (MIDL) (2018)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Novikov, A.A., Lenis, D., Major, D., Hladuvka, J., Wimmer, M., Bühler, K.: Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans. Med. Imaging 37(8), 1865–1876 (2018)
Payer, C., Štern, D., Bischof, H., Urschler, M.: Multi-label whole heart segmentation using cnns and anatomical label configurations. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 190–198. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_20
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
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Neural Information Processing Systems (NIPS) (2017)
Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule. Am. J. Roentgenol. 174(1), 71–74 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bonheur, S., Štern, D., Payer, C., Pienn, M., Olschewski, H., Urschler, M. (2019). Matwo-CapsNet: A Multi-label Semantic Segmentation Capsules Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_74
Download citation
DOI: https://doi.org/10.1007/978-3-030-32254-0_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32253-3
Online ISBN: 978-3-030-32254-0
eBook Packages: Computer ScienceComputer Science (R0)