Abstract
Automatic localization and segmentation of intervertebral discs (IVDs) from volumetric magnetic resonance (MR) images is important for spine disease diagnosis. It dramatically alleviates the workload of radiologists given that the traditional manual annotation is time-consuming and error-prone with limited reproducibility. Compared with single modality data, multi-modality MR images are able to provide complementary information. However, how to effectively integrate them to generate more accurate segmentation results still remains open for studies. In this paper, we introduce a multi-scale and modality dropout learning framework to segment IVDs from four-modality MR images. Specifically, we design a 3D fully convolutional network which takes multiple scales of images as input and merges these pathways at higher layers to jointly integrate multi-scale information. Furthermore, in order to harness the complementary information from different modalities, we propose a modality dropout strategy to alleviate the co-adaption issue during the training. We evaluated our method on the MICCAI 2016 Challenge on Automatic Intervertebral Disc Localization and Segmentation from 3D Multi-modality MR Images. Our method achieved the best overall performance with the mean segmentation Dice as 91.2% and localization error as 0.62 mm, which demonstrated the superiority of our proposed framework.
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References
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). doi:10.1007/978-3-642-22092-0_19
Cai, Y., Landis, M., Laidley, D.T., Kornecki, A., Lum, A., Li, S.: Multi-modal vertebrae recognition using transformed deep convolution network. Comput. Med. Imaging Graph. 51, 11–19 (2016)
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)
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). doi:10.1007/978-3-319-43775-0_34
Chen, H., Dou, Q., Yu, L., Heng, P.A.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016)
Chen, H., Shen, C., Qin, J., Ni, D., Shi, L., Cheng, J.C.Y., Heng, P.-A.: Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 515–522. Springer, Cham (2015). doi:10.1007/978-3-319-24553-9_63
Chevrefils, C., Chériet, F., Grimard, G., Aubin, C.-E.: Watershed segmentation of intervertebral disk and spinal canal from MRI images. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1017–1027. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74260-9_90
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
Jamaludin, A., Kadir, T., Zisserman, A.: SpineNet: automatically pinpointing classification evidence in spinal MRIs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 166–175. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_20
Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., Glocker, B.: Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. Ischemic Stroke Lesion Segmen. 13 (2015)
Law, M.W., Tay, K., Leung, A., Garvin, G.J., Li, S.: Intervertebral disc segmentation in MR images using anisotropic oriented flux. Med. Image Anal. 17(1), 43–61 (2013)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wang, Z., Zhen, X., Tay, K., Osman, S., Romano, W., Li, S.: Regression segmentation for spinal images. IEEE Trans. Med. Imaging 34(8), 1640–1648 (2015)
Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., Shen, D.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)
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Li, X., Dou, Q., Chen, H., Fu, CW., Heng, PA. (2016). Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science(), vol 10182. Springer, Cham. https://doi.org/10.1007/978-3-319-55050-3_8
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