TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling
- 4.1k Downloads
Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning based methods treat vascular tree reconstruction as a semantic segmentation problem. However, vessels such as hepatic and portal vein look very similar locally and need to be traced to their source for robust label assignment. Therefore, semantic segmentation by looking at local 3D patch results in noisy misclassifications. To tackle this, we propose a novel multi-task deep learning architecture for vessel tree reconstruction. The network architecture simultaneously solves the task of detecting voxels on vascular centerlines (i.e. nodes) and estimates connectivity between center-voxels (edges) in the tree structure to be reconstructed. Further, we propose a novel connectivity metric which considers both inter-class distance and intra-class topological distance between center-voxel pairs. Vascular trees are reconstructed starting from the vessel source using the learned connectivity metric using the shortest path tree algorithm. A thorough evaluation on public IRCAD dataset shows that the proposed method considerably outperforms existing semantic segmentation based methods. To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.
KeywordsDeep learning Vessel tree reconstruction Vessel segmentation Liver vessel Centerline detection Computed tomography
This research was done in cooperation with the Hepato-Biliary-Pancreatic Surgery Division, the University of Tokyo Hospital. We would like to thank Prof. Kiyoshi Hasegawa, Dr. Junichi Kaneko, Dr. Ryugen Takahashi, and Dr. Yusuke Kazami for their valuable advice and curating a liver vessel dataset.
- 3.Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
- 4.De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation with a discriminative loss function (2017). arXiv preprint arXiv:1708.02551
- 5.Fathi, A., et al.: Semantic instance segmentation via deep metric learning (2017). arXiv preprint arXiv:1703.10277
- 8.Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
- 10.IRCAD: Ircad dataset for liver vessel segmentation, March 2020. https://www.ircad.fr/research/3d-ircadb-01/
- 12.Keshwani, D., Kitamura, Y., Li, Y.: Computation of total kidney volume from CT images in autosomal dominant polycystic Kidney disease using multi-task 3D convolutional neural networks. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 380–388. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_44CrossRefGoogle Scholar
- 13.Kitrungrotsakul, T., Han, X.H., Iwamoto, Y., Foruzan, A.H., Lin, L., Chen, Y.W.: Robust hepatic vessel segmentation using multi deep convolution network. In: Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 10137, p. 1013711. International Society for Optics and Photonics (2017)Google Scholar
- 15.Kong, S., Fowlkes, C.C.: Recurrent pixel embedding for instance grouping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9018–9028 (2018)Google Scholar
- 16.Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
- 18.Payer, C., Štern, D., Neff, T., Bischof, H., Urschler, M.: Instance segmentation and tracking with cosine embeddings and recurrent hourglass networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_1CrossRefGoogle Scholar
- 19.Wakabayashi, G., et al.: Recommendations for laparoscopic liver resection: a report from the second international consensus conference held in morioka. Ann. Surg. 261(4), 619–629 (2015)Google Scholar