Zusammenfassung
Convolutional neural networks are currently the best working solution for automatic liver segmentation. Generally, each convolutional layer processes all feature maps from the previous layer. We show that the introduction of sparsely connected convolutional layers into the U-Net architecture can benefit the quality of liver segmentation and results in the increase of the dice coeffcient by 0:32% and a reduction of the mean surface distance by 3:84 mm on the LiTS data. Evaluation on the IRCAD data set with the application of post-processing showed a 0:70% higher Dice coeffcient and a 0:26 mm lower mean surface distance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Christ P, Ettlinger F, Lipkova J, et al.. LiTS: liver tumor segmentation challenge; 2017. Available from: http://www.lits-challenge.com/.
Han X. Automatic liver lesion segmentation using a deep convolutional neural network method. CoRR. 2017;abs/1704.07239.
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Proc MICCAI. 2015; p. 234–241.
Changpinyo S, Sandler M, Zhmoginov A. The power of sparsity in convolutional neural networks. CoRR. 2017;abs/1702.06257.
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proc CVPR. 2015; p. 1–9.
3D-IRCADb-01; 2018. Available from: https://www.ircad.fr/research/3d-ircadb-01/.
Salehi SSM, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. Mach Learn Med Imaging. 2017; p. 379–387.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Schnurr, AK., Schad, L.R., Zöllner, F.G. (2019). Sparsely Connected Convolutional Layers in CNNs for Liver Segmentation in CT. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-658-25326-4_20
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-25325-7
Online ISBN: 978-3-658-25326-4
eBook Packages: Computer Science and Engineering (German Language)