Abstract
Breast lesion segmentation result has a huge impact on the subsequent clinical analysis, and therefore it is of great importance for image-based diagnosis. In this paper, we propose a novel end-to-end network utilizing both spatial and temporal features for fully automated breast lesion segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Our network is based on a modified convolutional neural network and a recurrent neural network, and it is capable of unearthing rich spatio-temporal features. In our network, a multi-pathway structure and a fusion operator are introduced to acquire 3D information of different tissues, which is helpful for reducing false positive segmentation while boosting accuracy. Experimental results demonstrate that the proposed network produces a significantly more accurate result for lesion segmentation on our evaluation dataset, achieving 0.7588 dice coefficient and 0.7390 positive predictive value.
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References
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, vol. 16, pp. 265–283 (2016)
Amit, G., et al.: Hybrid mass detection in breast MRI combining unsupervised saliency analysis and deep learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 594–602. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_68
Arslan, G., Çelik, L., Çubuk, R., Çelik, L., Atasoy, M.M.: Background parenchymal enhancement: is it just an innocent effect of estrogen on the breast? Diagn. Interv. Radiol. 23(6), 414 (2017)
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or \(-\)1. arXiv preprint arXiv:1602.02830 (2016)
Graves, A.: Supervised Sequence Labelling With Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-642-24797-2
Huang, G., Liu, Z., Der Maaten, L.V., Weinberger, K.Q.: Densely connected convolutional networks. In: Computer Vision and Pattern Recognition, pp. 2261–2269 (2017)
Huang, Q., Yang, J.: A multistage target tracker in IR image sequences. Infrared Phys. Technol. 65(7), 122–128 (2014)
Jayender, J., Chikarmane, S., Jolesz, F.A., Gombos, E.: Automatic segmentation of invasive breast carcinomas from DCE-MRI using time series analysis. J. Magn. Reson. Imaging JMRI 40(2), 467–75 (2013)
Jin, Q., Grama, I., Kervrann, C., Liu, Q.: Nonlocal means and optimal weights for noise removal. SIAM J. Imaging Sci. 10(4), 1878–1920 (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lehman, C.D., et al.: MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. N. Engl. J. Med. 356(13), 1295–1303 (2007)
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Detnet: A backbone network for object detection. arXiv preprint arXiv:1804.06215 (2018)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Maicas, G., Carneiro, G., Bradley, A.P.: Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In: IEEE International Symposium on Biomedical Imaging, pp. 305–309 (2017)
Maicas, G., Carneiro, G., Bradley, A.P., Nascimento, J.C., Reid, I.: Deep reinforcement learning for active breast lesion detection from DCE-MRI. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 665–673. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_76
Marrone, S., Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., Sansone, C.: Automatic lesion detection in breast DCE-MRI. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 359–368. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7_37
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV) 2016, pp. 565–571. IEEE (2016)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
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
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA Cancer J. Clin. 54(1), 8–29 (2015)
Siu, A.L.: Screening for breast cancer: us preventive services task force recommendation statement. Ann. Intern. Med. 164(4), 279–296 (2016)
Wu, H., Gallego-Ortiz, C., Martel, A.: Deep artificial neural network approach to automated lesion segmentation in breast. In: Proceedings of the 3rd MICCAI Workshop on Breast Image Analysis, pp. 73–80 (2015)
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Yuan, Y., Giger, M.L., Hui, L., Suzuki, K., Sennett, C.: A dual-stage method for lesion segmentation on digital mammograms. Med. Phys. 34(11), 4180–4193 (2007)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Computer Vision and Pattern Recognition, pp. 6230–6239 (2017)
Acknowledgments
This research is partly supported by NSFC, China (No: 61572315, 6151101179) and 973 Plan, China (No. 2015CB856004).
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Chen, M., Zheng, H., Lu, C., Tu, E., Yang, J., Kasabov, N. (2018). A Spatio-Temporal Fully Convolutional Network for Breast Lesion Segmentation in DCE-MRI. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_32
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