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2019 | OriginalPaper | Buchkapitel

Deep Volumetric Shape Learning for Semantic Segmentation of the Hip Joint from 3D MR Images

verfasst von : Guodong Zeng, Guoyan Zheng

Erschienen in: Computational Methods and Clinical Applications in Musculoskeletal Imaging

Verlag: Springer International Publishing

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Abstract

This paper addresses the problem of segmentation of the hip joint including both the acetabulum and the proximal femur in three-dimensional magnetic resonance images. We propose a fully convolutional volumetric auto encoder that learns a volumetric representation from manual segmentation in order to regularize the segmentation results obtained from a fully convolutional network. We further introduce a super resolution network to improve the segmentation accuracy. Comprehensive results obtained from 24 patient data demonstrated the effectiveness of the proposed framework.

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Laborie, L., Lehmann, T., Engesæter, I., Eastwood, D., Engesæter, L., Rosendahl, K.: Prevalence of radiographic findings thought to be associated with femoroacetabular impingement in a population-based cohort of 2081 healthy young adults. Radiology 260(2), 494–502 (2011). https://doi.org/10.1148/radiol.11102354CrossRef

Leunig, M., Beaulé, P., Ganz, R.: The concept of femoroacetabular impingement: current status and future perspectives. Clin. Orthop. Relat. Res. 467(3), 616–622 (2009). https://doi.org/10.1007/s11999-008-0646-0CrossRef

Clohisy, J., Knaus, E., Hunt, D., Lesher, J., Harris-Hayes, M., Prather, H.: Clinical presentation of patients with symptomatic anterior hip impingement. Clin. Orthop. Relat. Res. 467(3), 638–644 (2009). https://doi.org/10.1007/s11999-008-0680-yCrossRef

Perdikakis, E., Karachalios, T., Katonis, P., Karantanas, A.: Comparison of MR-arthrography and MDCT-arthrography for detection of labral and articular cartilage hip pathology. Skeletal Radiol. 40(11), 1441–1447 (2011). https://doi.org/10.1007/s00256-011-1111-9CrossRef

Xia, Y., Fripp, J., Chandra, S., Schwarz, R., Engstrom, C., Crozier, S.: Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys. Med. Biol. 58(20), 7375–7390 (2013). https://doi.org/10.1088/0031-9155/58/20/7375CrossRef

Xia, Y., Chandra, S., Engstrom, C., Strudwick, M., Crozier, S., Fripp, J.: Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching. Phys. Med. Biol. 59(23), 7245–7266 (2014). https://doi.org/10.1088/0031-9155/59/23/7245CrossRef

Gilles, B., Magnenat-Thalmann, N.: Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations. Med. Image Anal. 14(3), 291–302 (2010). https://doi.org/10.1016/j.media.2010.01.006CrossRef

Arezoomand, S., Lee, W., Rakhra, K., Beaulé, P.: A 3D active model framework for segmentation of proximal femur in MR images. Int. J. Comput. Assist. Radiol. Surg. 10(1), 55–66 (2015). https://doi.org/10.1007/s11548-014-1125-6CrossRef

Chandra, S., Xia, Y., Engstrom, C., Crozier, S., Schwarz, R., Fripp, J.: Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med. Image Anal. 18(3), 567–578 (2014). https://doi.org/10.1016/j.media.2014.02.002CrossRef

10.

Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., et al. (eds.) Proceedings of Neural Information Processing Systems – NIPS 2012, vol. 25, pp. 1097–1105. NIPS (2012)

11.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition – CVPR 2015, pp. 3431–3440. IEEE (2015). https://doi.org/10.1109/CVPR.2015.7298965

12.

Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 246–253. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_31CrossRef

13.

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_49CrossRef

14.

Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of 4th International Conference on 3D Vision – 3DV 2016, pp. 565–571. IEEE (2016). https://doi.org/10.1109/3DV.2016.79

15.

Dou, Q., et al.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017). https://doi.org/10.1016/j.media.2017.05.001CrossRef

16.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of 32nd International Conference on Machine Learning – ICML 2015, vol. 37, pp. 448–456. PLMR (2015)

17.

Dahl, G., Sainath, T., Hinton, G.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP 2013, pp. 8609–8613. IEEE (2013). https://doi.org/10.1109/ICASSP.2013.6639346

18.

Karasawa, K., et al.: Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med. Image Anal. 39, 18–28 (2017). https://doi.org/10.1016/j.media.2017.03.006CrossRef

19.

Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993). https://doi.org/10.1109/34.232073CrossRef

20.

Yeghiazaryan, V., Voiculescu, I.: An overview of current evaluation methods used in medical image segmentation. Technical report CS-RR-15-08, University of Oxford, Department of Computer Science, UK (2015)

21.

Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of 23rd International Conference on Machine Learning – ICML 2006, pp. 233–240. ACM (2006). https://doi.org/10.1145/1143844.1143874

22.

Ravishankar, H., Venkataramani, R., Thiruvenkadam, S., Sudhakar, P., Vaidya, V.: Learning and incorporating shape models for semantic segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 203–211. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_24CrossRef

23.

Nosrati, M., Hamarneh, G.: Incorporating prior knowledge in medical image segmentation: a survey. arXiv:1607.01092 (2016). https://arxiv.org/abs/1607.01092

Titel: Deep Volumetric Shape Learning for Semantic Segmentation of the Hip Joint from 3D MR Images
verfasst von: Guodong Zeng
Guoyan Zheng
Verlag: Springer International Publishing
Buch: Computational Methods and Clinical Applications in Musculoskeletal Imaging
Print ISBN: 978-3-030-11165-6

Electronic ISBN: 978-3-030-11166-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-11166-3_4

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