Abstract
Due to the shortage and uneven distribution of medical resources all over the world, breast cancer diagnosis and treatment is a fundamental but vital problem, especially in developing countries. Breast ultrasound image classification and segmentation method by using Convolutional Neural Networks (CNN) can be a new efficient solution in early analysis and diagnosis. What’s more, the diagnosing of diversity of cancers is challenge in itself and the training of data-driven based CNN model also highly relay on dataset. In this paper, we first build a breast ultrasound dataset (with 1418 normal and 1182 cancerous samples) labeled by three radiologists from XiangYa Hospital of Hunan Province. And then, we propose a two-stage Computer-Aided Diagnosis (CAD) system to diagnose the breast cancer automatically. Firstly, the system utilize a pre-trained ResNet generated with transfer learning approach to excluded normal candidates, and then use an improved Mask R-CNN model for the accurate tumor segmentation. Experimental results show that the proposed system can achieve 98.72% precision and 98.05% recall for classification, and 85% (1.2% improvement) mAP and 82.7% (3.1% improvement) F1-Measure than the original Mask R-CNN model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Akin, O., et al.: Advances in oncologic imaging: update on 5 common cancers. CA: Cancer J. Clin. 62(6), 364 (2012)
Arbelaez, P., Maire, M., Fowlkes, C.C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Huynh, B., Drukker, K., Giger, M.: MO-DE-207B-06: computer-aided diagnosis of breast ultrasound images using transfer learning from deep convolutional neural networks. Med. Phys. 43, 3705 (2016)
Byra, M., Nowicki, A., Wroblewskapiotrzkowska, H., Dobruchsobczak, K.: Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters. Med. Phys. 43(10), 5561–5569 (2016)
Cai, L., Wang, X., Wang, Y., Guo, Y., Yu, J., Wang, Y.: Robust phase-based texture descriptor for classification of breast ultrasound images. Biomed. Eng. Online 14(1), 26 (2015)
Cheng, H.D., Shan, J., Ju, W., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognit. 43(1), 299–317 (2010)
Dhungel, N., Carneiro, G., Bradley, A.P.: Deep learning and structured prediction for the segmentation of mass in mammograms. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 605–612. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_74
Drukker, K., Gruszauskas, N.P., Sennett, C.A., Giger, M.L.: Breast us computer-aided diagnosis workstation: performance with a large clinical diagnostic population. Radiology 248(2), 392–397 (2008)
Flores, W.G., Pereira, W.C.A., Infantosi, A.F.C.: Breast ultrasound despeckling using anisotropic diffusion guided by texture descriptors. Ultrasound Med. Biol. 40(11), 2609–2621 (2014)
Flores, W.G., Pereira, W.C.A., Infantosi, A.F.C.: Improving classification performance of breast lesions on ultrasonography. Pattern Recognit. 48(4), 1125–1136 (2015)
Gomez, W., Pereira, W.C.A., Infantosi, A.F.C.: Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans. Med. Imaging 31(10), 1889–1899 (2012)
Gomez, W., Pereira, W.C.A., Infantosi, A.F.C.: Evolutionary pulse-coupled neural network for segmenting breast lesions on ultrasonography. Neurocomputing 175, 877–887 (2016)
He, K., Gkioxari, G., Dollr, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Horsch, K., Giger, M.L., Venta, L.A., Vyborny, C.J.: Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29(2), 157–164 (2002)
Joo, S., Yang, Y.S., Moon, W.K., Kim, H.C.: Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans. Med. Imaging 23(10), 1292–1300 (2004)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks, pp. 1097–1105 (2012)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollr, P.: Focal loss for dense object detection, pp. 2999–3007 (2017)
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)
Marcomini, K.D., Carneiro, A.A.O., Schiabel, H.: Application of artificial neural network models in segmentation and classification of nodules in breast ultrasound digital images. Int. J. Biomed. Imaging 2016, 7987212 (2016)
Moon, W.K., Lo, C.M., Chang, J.M., Huang, C.S., Chen, J.H., Chang, R.F.: Computer-aided classification of breast masses using speckle features of automated breast ultrasound images. Med. Phys. 39(10), 6465–6473 (2012)
Pons, G., Marti, J., Marti, R., Ganau, S., Vilanova, J.C., Noble, J.A.: Evaluating lesion segmentation on breast sonography as related to lesion type. J. Ultrasound Med. 32(9), 1659–1670 (2013)
Rodrigues, R., Braz, R., Pereira, M., Moutinho, J., Pinheiro, A.M.: A two-step segmentation method for breast ultrasound masses based on multi-resolution analysis. Ultrasound Med. Biol. 41(6), 1737–1748 (2015)
Rodrigues, R., Pinheiro, A.M.G., Braz, R., Pereira, M., Moutinho, J.: Towards breast ultrasound image segmentation using multi-resolution pixel descriptors, pp. 2833–2836 (2012)
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
Sadek, I., Elawady, M., Stefanovski, V.: Automated breast lesion segmentation in ultrasound images. Computer Vision and Pattern Recognition. arXiv:1609.08364 (2016)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Shi, J., Zhou, S., Liu, X., Zhang, Q., Lu, M., Wang, T.: Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing 194, 87–94 (2016)
Su, H., Liu, F., Xie, Y., Xing, F., Meyyappan, S., Yang, L.: Region segmentation in histopathological breast cancer images using deep convolutional neural network, pp. 55–58 (2015)
Takemura, A., Shimizu, A., Hamamoto, K.: Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the adaboost algorithm with feature selection. IEEE Trans. Med. Imaging 29(3), 598–609 (2010)
Uniyal, N., et al.: Ultrasound RF time series for classification of breast lesions. IEEE Trans. Med. Imaging 34(2), 652–661 (2015)
Wang, D., Shi, L., Heng, P.A.: Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72, 3296–3302 (2009)
Wang, W., Zhu, L., Qin, J., Chui, Y.P., Li, B.N., Heng, P.A.: Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion. Opt. Lasers Eng. 54, 105–116 (2014)
Wang, Z., Yu, G., Kang, Y., Zhao, Y., Qu, Q.: Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing 128(5), 175–184 (2014)
Xi, X., et al.: Breast tumor segmentation with prior knowledge learning. Neurocomputing 237, 145–157 (2017)
Xian, M., Huang, J., Zhang, Y., Tang, X.: Multiple-domain knowledge based MRF model for tumor segmentation in breast ultrasound images, pp. 2021–2024 (2012)
Shi, X., Cheng, H.D., Hu, L.: Mass detection and classification in breast ultrasound images using fuzzy SVM. In: Proceedings of Joint Conference on Information Sciences (2006)
Yap, M.H., Edirisinghe, E., Bez, H.: Processed images in human perception: a case study in ultrasound breast imaging. Eur. J. Radiol. 73(3), 682–687 (2010)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Acknowledgement
This work has been supported by National Key R&D Program of China (2017YFB1301100), National Natural Science Foundation of China (61572060, 61772060, 61728201) and CERNET Innovation Project (NGII20160316, NGII20170315).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Xie, X., Shi, F., Niu, J., Tang, X. (2018). Breast Ultrasound Image Classification and Segmentation Using Convolutional Neural Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-00764-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00763-8
Online ISBN: 978-3-030-00764-5
eBook Packages: Computer ScienceComputer Science (R0)