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2020 | OriginalPaper | Chapter

A Second-Order Subregion Pooling Network for Breast Lesion Segmentation in Ultrasound

Authors : Lei Zhu, Rongzhen Chen, Huazhu Fu, Cong Xie, Liansheng Wang, Liang Wan, Pheng-Ann Heng

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Publisher: Springer International Publishing

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Abstract

Breast lesion segmentation in ultrasound images is a fundamental task for clinical diagnosis of the disease. Unfortunately, existing methods mainly rely on the entire image to learn the global context information, which neglects the spatial relation and results in ambiguity in the segmentation results. In this paper, we propose a novel second-order subregion pooling network (\(S^2P\)-Net) for boosting the breast lesion segmentation in ultrasound images. In our \(S^2P\)-Net, an attention-weighted subregion pooling (ASP) module is introduced in each encoder block of segmentation network to refine features by aggregating global features from the whole image and local information of subregions. Moreover, in each subregion, a guided multi-dimension second-order pooling (GMP) block is designed to leverage additional guidance information and multiple feature dimensions to learn powerful second-order covariance representations. Experimental results on two datasets demonstrate that our proposed \(S^2P\)-Net outperforms state-of-the-art methods.

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previous chapter 2D X-Ray Mammogram and 3D Breast MRI Registration

next chapter Multi-scale Gradational-Order Fusion Framework for Breast Lesions Classification Using Ultrasound Images

Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)CrossRef

Boukerroui, D., Basset, O., Guerin, N., Baskurt, A.: Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. Eur. J. Ultrasound 8(2), 135–144 (1998)CrossRef

Chang, H.H., Zhuang, A.H., Valentino, D.J., Chu, W.C.: Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 47(1), 122–135 (2009)CrossRef

Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV, pp. 801–818 (2018)

Gao, Z., Xie, J., Wang, Q., Li, P.: Global second-order pooling convolutional networks. In: CVPR, pp. 3024–3033 (2019)

Gómez-Flores, W., Ruiz-Ortega, B.A.: New fully automated method for segmentation of breast lesions on ultrasound based on texture analysis. Ultrasound Med. Biol. 42(7), 1637–1650 (2016)CrossRef

Gu, Z., et al.: CE-Net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)CrossRef

He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2012)CrossRef

Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)

10.

Lei, B., et al.: Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder-decoder network. Neurocomputing 321, 178–186 (2018)CrossRef

11.

Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117–2125 (2017)

12.

Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)

13.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)

14.

Madabhushi, A., Metaxas, D.N.: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. Med. Imaging 22(2), 155–169 (2003)CrossRef

15.

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_28CrossRef

16.

Shan, J., Cheng, H., Wang, Y.: Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med. Biol. 38(2), 262–275 (2012)CrossRef

17.

Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA: Cancer J. Clin. 67(1), 7–30 (2017)

18.

Stavros, A.T., Thickman, D., Rapp, C.L., Dennis, M.A., Parker, S.H., Sisney, G.A.: Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 196(1), 123–134 (1995)CrossRef

19.

Wang, Y., et al.: Deep attentional features for prostate segmentation in ultrasound. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 523–530. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_60CrossRef

20.

Xian, M., Zhang, Y., Cheng, H.D.: Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains. Pattern Recogn. 48(2), 485–497 (2015)CrossRef

21.

Xu, Y., Wang, Y., Yuan, J., Cheng, Q., Wang, X., Carson, P.L.: Medical breast ultrasound image segmentation by machine learning. Ultrasonics 91, 1–9 (2019)CrossRef

22.

Yap, M.H., et al.: Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 22(4), 1218–1226 (2018)CrossRef

23.

Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 815–830. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_53CrossRef

24.

Zhang, Z., Fu, H., Dai, H., Shen, J., Pang, Y., Shao, L.: ET-Net: A generic edge-aTtention guidance network for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 442–450. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_49CrossRef

25.

Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1CrossRef

26.

Zhu, L., et al.: Aggregating attentional dilated features for salient object detection. IEEE Trans. Circ. Syst. Video Technol. PP(99), 1 (2019)CrossRef

27.

Zhu, L., et al.: Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 121–136 (2018)

Title: A Second-Order Subregion Pooling Network for Breast Lesion Segmentation in Ultrasound
Authors: Lei Zhu
Rongzhen Chen
Huazhu Fu
Cong Xie
Liansheng Wang
Liang Wan
Pheng-Ann Heng
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Print ISBN: 978-3-030-59724-5

Electronic ISBN: 978-3-030-59725-2

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-59725-2_16

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