新型乳腺磁共振增强图像肿瘤区域的自动分割模型

doi:10.3969/j.issn.0258-8021.2019.01.004

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Abstract Breast cancer can be mainly classified into two kinds: mass-like and non-mass-like on enhanced breast images. Owing to the small area of breast cancer, along with the huge difference between the shape of mass-like and non-mass-like and the self complexity of non-mass-like, it is hard to segment the accurate area of breast tumor. To solve these problems, this paper proposed a novel deep learning model of rough detection and fine segmentation. Before precise segmentation, rough detection for the cancer region was firstly processed for potential region of the tumor. On the basis of rough detection, we used SegNet for fine segmentation to achieve the best performance of the algorithm. In order to test the effectiveness of proposed method (YOLOv2+SegNet), we picked 560 magnetic resonance imaging (MRI) images of breast cance out of the dataset collected from the hospital for training and testing (415 images for training and 145 for testing). For more comprehensive analysis, experiments were set to analyze three different conditions, such as mass-like, non-mass-like and the mix of mass-like and non-mass-like. From the results, the established method improved 10% under each condition and improved a lot compared with the traditional C-V model, fuzzy C mean clustering, active contour model for spectral mapping and deep model of U-net or DeepLab.

Key words： deep detection and segmentation model magnetic resonance imaging breast cancer mass-like non-mass-like

Received: 02 March 2018

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R318

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	Articles by authors
	Ma Wei
	Liu Hongli
	Sun Mingjian
	Xu Jun
	Jiang Yanni

Cite this article:

Ma Wei,Liu Hongli,Sun Mingjian, et al. A Novel Automated Tumor Segmentation Model for Enhanced Breast MRI[J]. Chinese Journal of Biomedical Engineering, 2019, 38(1): 28-34.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.01.004 OR http://cjbme.csbme.org/EN/Y2019/V38/I1/28

[1] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016[J]. CA: A Cancer Journal for Clinicians, 2016, 66(1): 7-30.
[2] Chen Wanqing, Zheng Rongshou, Baade PD, et al. Cancer statistics in China, 2015[J]. CA: A Cancer Journal for Clinicians, 2016, 66(2): 115-132.
[3] Moftah HM, Azar AT, Al-Shammari ET, et al. Adaptive k-means clustering algorithm for MR breast image segmentation[J]. Neural Computing & Applications, 2014, 24(7-8):1917-1928.
[4] Aslam A, Khan E, Beg MMS. Improved edge detection algorithm for brain tumor segmentation[J]. Procedia Computer Science, 2015, 58:430-437.
[5] Alfaris AQ, Ngah UK, Isa NAM, et al. Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG)[J]. Journal of Digital Imaging, 2014, 27(1):133.
[6] Zadeh HG, Haddadnia J, Montazeri A. A model for diagnosing breast cancerous tissue from thermal images using active contour and lyapunov exponent[J]. Iranian Journal of Public Health, 2016, 45(5):657-669.
[7] Li Bang Nan, Chui Chee Kong, et al. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation[J]. Computers in Biology and Medicine, 2011, 41(1): 1-10.
[8] Tuwohingide D, Fatichah C. Spatial Fuzzy C-means dan rapid region merging untuk pemisahan sel kanker payudara[J]. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 2017, 6(1):294-294.
[9] Su Hai, Liu Fujun, Xie Yuanpu, et al. Region segmentation in histopathological breast cancer images using deep convolutional neural network[C]//International Symposium on Biomedical Imaging. California:IEEE, 2015:55-58.
[10] Wang J, Zhang Z, Li B, et al. An enhanced fall detection system for elderly person monitoring using consumer home networks[J]. IEEE Transactions on Consumer Electronics, 2014, 60(1): 23-29.
[11] He Kaiming, Gkioxari G, Dollar P, et al. Mask R-CNN[J]. arXiv:1703.06870,2017.
[12] MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017[J]. Radiology, 2017, 284(1): 228-243.
[13] Redmon J, Farhadi A. YOLO9000: Better, faster, stronger[EB/OL]. https://arxiv.org/cs/1612.08242. 2016-12-05/2017-10-04.
[14] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]// Computer Vision and Pattern Recognition. California: IEEE, 2016:779-788.
[15] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision. California:IEEE, 2015: 1520-1528.
[16] Ren Shaoqing, Girshick R, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
[17] Hartigan JA, Wong MA. Algorithm AS 136: A K-means clustering algorithm[J]. Journal of the Royal Statistical Society, 1979, 28(1):100-108.
[18] Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models[J]. International Journal of Computer Vision, 1988, 1(4):321-331.
[19] Xu Jun, Janowczyk A, Chandran S, et al. A high-throughput active contour scheme for segmentation of histopathological imagery[J]. Medical Image Analysis, 2011, 15(6):851-862.
[20] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for scene segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39:2481-2495.
[21] Salamon J, Bello JP. Deep convolutional neural networks and data augmentation for environmental sound classification[J]. IEEE Signal Processing Letters, 2017, 24: 279-283.
[22] CiresAn D, Meier U, Masci J, et al. Multi-column deep neural network for traffic sign classification[J]. Neural Networks, 2012, 32: 333-338.
[23] Fedorov A, Beichel R, Kalpathycramer J, et al. 3D Slicer as an image computing platform for the quantitative imaging network[J]. Magnetic Resonance Imaging, 2012, 30:1323-1341.
[24] Boegel M, Hoelter P, Redel T, et al. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography[C]//?The 37th Annual International Conference of the IEEE EMBS. Milan: IEEE, 2015:2006-2009.
[25] Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications[J]. PNAS, 2001, 98:10869-10874.
[26] Sørlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets[J]. PNAS, 2003, 100:8418-8423.