Abstract
Visual cues of enforcing bilaterally symmetric anatomies as normal findings are widely used in clinical practice to disambiguate subtle abnormalities from medical images. So far, inadequate research attention has been received on effectively emulating this practice in computer-aided diagnosis (CAD) methods. In this work, we exploit semantic anatomical symmetry or asymmetry analysis in a complex CAD scenario, i.e., anterior pelvic fracture detection in trauma pelvic X-rays (PXRs), where semantically pathological (refer to as fracture) and non-pathological (e.g. pose) asymmetries both occur. Visually subtle yet pathologically critical fracture sites can be missed even by experienced clinicians, when limited diagnosis time is permitted in emergency care. We propose a novel fracture detection framework that builds upon a Siamese network enhanced with a spatial transformer layer to holistically analyze symmetric image features. Image features are spatially formatted to encode bilaterally symmetric anatomies. A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences). Our proposed method have been extensively evaluated on 2,359 PXRs from unique patients (the largest study to-date), and report an area under ROC curve score of 0.9771. This is the highest among state-of-the-art fracture detection methods, with improved clinical indications.
H. Chen and Y. Wang—Equal contribution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barman, A., Inam, M.E., Lee, S., Savitz, S., Sheth, S., Giancardo, L.: Determining ischemic stroke from CT-angiography imaging using symmetry-sensitive convolutional networks. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1873–1877, April 2019. https://doi.org/10.1109/ISBI.2019.8759475
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56
Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (1989). https://doi.org/10.1109/34.24792
Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vayá, M.: PadChest: a large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2019)
Chen, H., Miao, S., Xu, D., Hager, G.D., Harrison, A.P.: Deep hierarchical multi-label classification of chest x-ray images. In: Cardoso, M.J., et al. (eds.) Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, 08–10 July 2019, vol. 102, pp. 109–120. PMLR, London (2019). http://proceedings.mlr.press/v102/chen19a.html
Cheng, C.-T., et al.: Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur. Radiol. 29(10), 5469–5477 (2019). https://doi.org/10.1007/s00330-019-06167-y
Clohisy, J.C., et al.: A systematic approach to the plain radiographic evaluation of the young adult hip. J. Bone Joint Surg. Am. 90(Suppl 4), 47 (2008)
Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A.P., Palmer, L.J.: Detecting hip fractures with radiologist-level performance using deep neural networks. CoRR abs/1711.06504 (2017). http://arxiv.org/abs/1711.06504
Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1763–1771 (2017)
Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: The IEEE International Conference on Computer Vision (ICCV), October 2017
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735–1742, June 2006. https://doi.org/10.1109/CVPR.2006.100
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) Similarity-Based Pattern Recognition. LNCS, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.: Densely connected convolutional networks. arxiv website. arxiv. org/abs/1608.06993. 24 August 2016
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016). http://arxiv.org/abs/1608.06993
Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. CoRR abs/1901.07031 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)
Konukoglu, E., Glocker, B., Criminisi, A., Pohl, K.M.: Wesd-weighted spectral distance for measuring shape dissimilarity. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2284–2297 (2012)
Li, W., et al.: Structured landmark detection via topology-adapting deep graph learning. arXiv preprint arXiv:2004.08190 (2020)
Li, Z., et al.: Thoracic disease identification and localization with limited supervision. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 8290–8299. IEEE (2018). https://doi.org/10.1109/CVPR.2018.00865
Ling, H., Gao, J., Kar, A., Chen, W., Fidler, S.: Fast interactive object annotation with curve-GCN. CoRR abs/1903.06874 (2019). http://arxiv.org/abs/1903.06874
Liu, C.F., et al.: Using deep siamese neural networks for detection of brain asymmetries associated with Alzheimer’s disease and mild cognitive impairment. Magn. Reson. Imaging (2019). https://doi.org/10.1016/j.mri.2019.07.003, http://www.sciencedirect.com/science/article/pii/S0730725X19300086
Liu, S.X.: Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: a review of the literature. J. Biomed. Inform. 42(6), 1056–1064 (2009)
Liu, Y., et al.: From unilateral to bilateral learning: detecting mammogram masses with contrasted bilateral network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 477–485. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_53
Lu, Y., et al.: Learning to segment anatomical structures accurately from one exemplar. arXiv preprint arXiv:2007.03052 (2020)
Melekhov, I., Kannala, J., Rahtu, E.: Siamese network features for image matching. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 378–383, December 2016. https://doi.org/10.1109/ICPR.2016.7899663
Paszke, A., et al.: Automatic differentiation in PyTorch (2017)
Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. CoRR abs/1711.05225 (2017). http://arxiv.org/abs/1711.05225
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., Komodakis, N.: A deep metric for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 10–18. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46726-9_2
Sun, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. CoRR abs/1406.4773 (2014). http://arxiv.org/abs/1406.4773
Varior, R.R., Haloi, M., Wang, G.: Gated siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_48
Wachinger, C., Golland, P., Kremen, W., Fischl, B., Reuter, M., Initiative, A.D.N., et al.: Brainprint: a discriminative characterization of brain morphology. NeuroImage 109, 232–248 (2015)
Wang, H., Xia, Y.: ChestNet: a deep neural network for classification of thoracic diseases on chest radiography. arXiv preprint arXiv:1807.03058 (2018)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Wang, Y., et al.: Weakly supervised universal fracture detection in pelvic x-rays. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 459–467. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_51
Xu, Z., et al.: Less is more: simultaneous view classification and landmark detection for abdominal ultrasound images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 711–719. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_79
Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353–4361 (2015)
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. CoRR abs/1512.04150 (2015). http://arxiv.org/abs/1512.04150
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, H. et al. (2020). Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-Ray Images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-58592-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58591-4
Online ISBN: 978-3-030-58592-1
eBook Packages: Computer ScienceComputer Science (R0)