Abstract
This paper addresses the challenging problem of segmentation of intervertebral discs (IVDs) in three-dimensional (3D) T2-weighted magnetic resonance (MR) images. We propose a deeply supervised multi-scale fully convolutional network for segmentation of IVDs in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Multi-scale deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on the MICCAI 2015 IVD segmentation challenge datasets. Our method achieved a mean Dice overlap coefficient of 92.0% and a mean average symmetric surface distance of 0.41 mm. The results achieved by our method are better than those achieved by the state-of-the-art methods.
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References
Modic, M., Ross, J.: Lumbar degenerative disk disease. Radiology 245(1), 43–61 (2007)
Parizel, P., Van Goethem, J., Van den Hauwe, L., Voormolen, M.: Degenerative disc disease. In: Van Goethem, J., et al. (eds.) Spinal Imaging, pp. 127–156. Medical Radiology. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-68483-1_6
An, H., Anderson, P., Haughton, V., Iatridis, J., Kang, J., Lotz, J., Natarajan, R., Oegema, T.J., Roughley, P., Setton, L., Urban, J., Videman, T., Andersson, G., Weinstein, J.: Introduction: disc degeneration: summary. Spine 29(23), 2677–2678 (2004)
Chevrefils, C., Cheriet, F., Aubin, C., Grimard, G.: Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from MR images. IEEE Trans. Inf Technol. Biomed. 13(4), 608–620 (2009)
Michopoulou, S., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Trans. Biomed. Eng. 56(9), 2225–2231 (2009)
Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22092-0_19
Neubert, A., Fripp, J., Engstrom, C., Schwarz, R., Lauer, L., Salvado, O., Crozier, S.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Phys. Med. Biol. 57(24), 8457–8376 (2012)
Law, M., Tay, K., Leung, A., Garvin, G., Li, S.: Intervertebral disc segmentation in MR images using anisotropic oriented flux. Med. Image Anal. 17(1), 43–61 (2013)
Zheng, G., Chu, C., Belavý, D., Ibragimov, B., Korez, R., Vrtovec, T., Hutt, H., Everson, R., Meakin, J., Andrade, I., Glocker, B., Chen, H., Dou, Q., Heng, P., Wang, C., Forsberg, D., Neubert, A., Fripp, J., Urschler, M., Stern, D., Wimmer, M., Novikov, A., Cheng, H., Armbrecht, G., Felsenberg, D., Li, S.: Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: a grand challenge. Med. Image Anal. 35, 327–344 (2017)
Zhan, Y., Maneesh, D., Harder, M., Zhou, X.S.: Robust MR spine detection using hierarchical learning and local articulated model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 141–148. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_18
Kelm, M., Wels, M., Zhou, S., Seifert, S., Suehling, M., Zheng, Y., Comaniciu, D.: Spine detection in CT and MR using iterated marginal space learning. Med. Image Anal. 17(8), 1283–1292 (2013)
Chen, C., Belavy, D., Yu, W., Chu, C., Armbrecht, G., Bansmann, M., Felsenberg, D., Zheng, G.: Localization and segmentation of 3D intervertebral discs in MR images by data driven estimation. IEEE Trans. Med. Imaging 34(8), 1719–1729 (2015)
Wang, Z., Zhen, X., Tay, K., Osman, S., Romano, W., Li, S.: Regression segmentation for M\(^3\) spinal images. IEEE Trans. Med. Imaging 34(8), 1640–1648 (2015)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
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)
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_31
Roth, H.R., et al.: A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 520–527. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10404-1_65
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 (2015)
Roth, H., Yao, J., Lu, L., Stieger, J., Burns, J., Summers, R.: Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications. In: Yao, J., et al. (eds.) Proceedings of 2nd MICCAI Workshop on Computational Methods and Clinical Applications for Spine CSI 2014, LNCVB, vol. 20, pp. 3–12. Springer (2015). https://doi.org/10.1007/978-3-319-14148-0_1
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_49
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)
Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., Heng, P.A.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017)
Chen, H., Dou, Q., Wang, X., Qin, J., Cheng, J.C.Y., Heng, P.-A.: 3D fully convolutional networks for intervertebral disc localization and segmentation. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, S.-L. (eds.) MIAR 2016. LNCS, vol. 9805, pp. 375–382. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43775-0_34
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 179–187. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_19
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2016, pp. 770–778. IEEE (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing. In: Proceedings of 32nd International Conference on Machine Learning - ICML 2015, vol. 37, pp. 448–456. PLMR (2015)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., et al. (eds.) Proceedings of Advances in Neural Information Processing Systems - NIPS 2014, pp. 3320–3328. MIT Press (2014)
Zeng, G., Yang, X., Li, J., Yu, L., Heng, P., Zheng, G.: 3D U-Net with multi-level deep supervision:fully automatic segmentation of proximal femur in 3D MR images. In: 8th MICCAI International Workshop on Machine Learning in Medical Imaging - MLMI 2017 (2017)
Fang, Q., Boas, D.: Tetrahedral mesh generation from volumetric binary and gray-scale images. In: Proceedings of 6th IEEE International Symposium on Biomedical Imaging - ISBI 2009, pp. 1142–1145. IEEE (2009)
Heimann, T., van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P., Chi, Y., Cordova, A., Dawant, B., Fidrich, M., Furst, J., Furukawa, D., Grenacher, L., Hornegger, J., Kainmüller, D., Kitney, R., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H., Nemeth, G., Raicu, D., Rau, A., van Rikxoort, E., Rousson, M., Rusko, L., Saddi, K., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J., Wimmer, A., Wolf, I.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)
Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching. J. ACM 45(6), 891–923 (1998)
Karasawa, K., Oda, M., Kitasaka, T., Misawa, K., Fujiwara, M., Chu, C., Zheng, G., Rueckert, D., Mori, K.: Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med. Image Anal. 39, 18–28 (2017)
Korez, R., Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T.: Deformable model-based segmentation of intervertebral discs from MR spine images by using the SSC descriptor. In: Vrtovec, T., Yao, J., Glocker, B., Klinder, T., Frangi, A., Zheng, G., Li, S. (eds.) CSI 2015. LNCS, vol. 9402, pp. 117–124. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41827-8_11
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Zeng, G., Zheng, G. (2018). DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science(), vol 10734. Springer, Cham. https://doi.org/10.1007/978-3-319-74113-0_13
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