Abstract
Automatic prostate segmentation from magnetic resonance images can assist in diagnosis and radiological planning. The extensive clinical application of this task has attracted the attention of researchers. However, due to noise, blurred boundaries and scale variation, it is very challenging to segment prostate from magnetic resonance images. We propose a cascade method for prostate segmentation. The model consists of two stage. In the first stage, a dense-unet model are used to obtain the initial segmentation results. In the second stage, the segmentation result of the first stage is used as prior knowledge, and another dense-unet is used to obtain more accurate segmentation results. The experimental results show that the proposed method can obtain more accurate segmentation results.
Keywords
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Vos, P., Barentsz, J., Karssemeijer, N., Huisman, H.: Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys. Med. Biol. 57(6), 1527 (2012)
Toth, R., et al.: Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI. Acad. Radiol. 18(6), 745–754 (2011)
Klein, S., vander Heide, U., Lipps, I., Vulpen, M., Staring, M., Pluim, J.: Automatic segmentation of the prostate in 3-D MR images by atlas matching using localized mutual information. Med. Phys. 35(4), 1407–1417 (2008)
Martin, S., Daanen, V., Troccaz, J.: Automated segmentation of the prostate 3-D MR images using a probabilistic atlas and a spatially constrained deformable model. Med. Phys. 37(4), 1579–1590 (2010)
Ou, Y., Doshi, J., Erus, G., Davatzikos, C.: Multi-atlas segmentation of the prostate: a zooming process with robust registration and atlas selection. In: 2012 MICCAI Grand Challenge: Prostate MR Image Segmentation (2012)
Yan, P., Cao, Y., Yuan, Y., Turkbey, B., Choyke, P.L.: Label image constrained multi-atlas selection. IEEE Trans. Cybernet. 45(6), 1158–1168 (2015)
Pasquier, D., Lacornerie, T., Vermandel, M., Rousseau, J., Lartigau, E., Betrouni, N.: Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 68(2), 592–600 (2007)
Makni, N., Puech, P., Lopes, R., Dewalle, A.: Combining a deformable model and a probabilistic framework for an automatic 3-D segmentation of prostate on MRI. Int. J. Comput. Assisted. Radiol. Surg. 4(2), 181–188 (2009)
Toth, R., Madabhushi, A.: Multifeature landmark-free active appearance models: Application to prostate MRI segmentation. IEEE Trans. Med. Imag 31(8), 1638–1650 (2012)
Moschidis E., Graham, J.: Automatic differential segmentation of the prostate in 3-D MRI using random forest classification and graph cuts optimization. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 1727–1730 (2012)
Zouqi M., Samarabandu, J.: Prostate segmentation from 2-D ultrasound images using graph cuts and domain knowledge. In: Proceedings of the Conference on Computer and Robot Vision, pp. 359–362 (2008)
Tian, Z., Liu, L., Zhang, Z., Fei, B.: Superpixel-based segmentation for 3D prostate MR images. IEEE Trans. Med. Imaging 35(3), 791–801 (2016)
Guo, Y., Gao, Y., Shen, D.: Deformable MR prostate segmentation via deep feature learning and sparse patch matching. IEEE Trans. Med. Imaging 35(4), 1077–1089 (2016)
Jia, H., Xia, Y., Song, Y., Cai, W., Fulham, M., Feng, D.D.: Atlas registration and en- semble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging. Neurocomputing 275, 1358–1369 (2017)
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)
Yu, L., Yang, X., Chen, H., Qin, J., Heng, P.-A.: Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images. In: AAAI, pp. 66–72 (2017)
Yan, K., Wang, X., Kim, J., et al.: A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput. Methods Programs Biomed. 170, 11–21 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Zhu, Q., Du, B., Turkbey, B., Choyke, P., Yan, P.: Exploiting interslice correlation for MRI prostate image segmentation, from recursive neural networks aspect. Complexity, vol. 10 (2018)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., Heng, P.: H-DenseUNet: hybrid densely connected UNet for liver and liver tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Yu, L., Cheng, J.-Z., Dou, Q., Yang, X., Chen, H., Qin, J., Heng, P.-A.: Automatic 3D cardiovascular MR segmentation with densely-connected volumetric ConvNets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 287–295. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_33
Chen, H., Dou, Q., Yu, L., Heng, P.-A.: VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. CoRR, abs/1608.06993 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Litjens, G., et al.: Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med. Image Anal. 18(2), 359–373 (2014)
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This work is supported by Shanghai Science and Technology Commission (grant No. 17511104203) and NSFC (grant NO. 61472087).
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Li, S., Chen, Y., Yang, S., Luo, W. (2019). Cascade Dense-Unet for Prostate Segmentation in MR Images. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_46
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