Abstract
While deep convolutional neural networks (CNN) have been successfully applied to 2D image analysis, it is still challenging to apply them to 3D medical images, especially when the within-slice resolution is much higher than the between-slice resolution. We propose a 3D Anisotropic Hybrid Network (AH-Net) that transfers convolutional features learned from 2D images to 3D anisotropic volumes. Such a transfer inherits the desired strong generalization capability for within-slice information while naturally exploiting between-slice information for more effective modelling. We experiment with the proposed 3D AH-Net on two different medical image analysis tasks, namely lesion detection from a Digital Breast Tomosynthesis volume, and liver and liver tumor segmentation from a Computed Tomography volume and obtain state-of-the-art results.
D. Xu—Equal contribution.
This is a preview of subscription content, access via your institution.
Buying options
References
Chen, J., Yang, L., Zhang, Y., Alber, M.S., Chen, D.Z.: Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation. In: NIPS (2016)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. ArXiv eprints arXiv:1606.06650 (2016)
Ghesu, F.C., Georgescu, B., Grbic, S., Maier, A.K., Hornegger, J., Comaniciu, D.: Robust multi-scale anatomical landmark detection in incomplete 3D-CT data. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 194–202. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_23
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR, pp. 770–778 (2016)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely Connected Convolutional Networks. ArXiv eprints arXiv:1608.06993 (2016)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. ArXiv eprints arXiv:1412.6980 (2014)
Lee, K., Zung, J., Li, P., Jain, V., Seung, H.S.: Superhuman Accuracy on the SNEMI3D Connectomics Challenge. ArXiv e-prints arXiv:1706.00120 (2017)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver Tumor Segmentation from CT Volumes. ArXiv e-prints arXiv:1709.07330 (2017)
Liu, F., Zhou, Z., Jang, H., Samsonov, A., Zhao, G., Kijowski, R.: Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal MR imaging. Magn. Reson. Med. (2017)
Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: Large Kernel Matters - Improve Semantic Segmentation by Global Convolutional Network. ArXiv eprints arXiv:1703.02719 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv eprints arXiv:1505.04597 (2015)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid Scene Parsing Network. ArXiv eprints arXiv:1612.01105 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, S. et al. (2018). 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science(), vol 11071. Springer, Cham. https://doi.org/10.1007/978-3-030-00934-2_94
Download citation
DOI: https://doi.org/10.1007/978-3-030-00934-2_94
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00933-5
Online ISBN: 978-3-030-00934-2
eBook Packages: Computer ScienceComputer Science (R0)