A Novel Automated Tumor Segmentation Model for Enhanced Breast MRI
Ma Wei1, Liu Hongli2, Sun Mingjian1, Xu Jun1*, Jiang Yanni2*
1Jiangsu Key Laboratory of Big Data Analysis, Nanjing University of Information Science and Technology, Nanjing 210044 China; 2Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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.
马伟, 刘鸿利, 孙明建, 徐军, 蒋燕妮. 新型乳腺磁共振增强图像肿瘤区域的自动分割模型[J]. 中国生物医学工程学报, 2019, 38(1): 28-34.
Ma Wei, Liu Hongli, Sun Mingjian, Xu Jun, Jiang Yanni. A Novel Automated Tumor Segmentation Model for Enhanced Breast MRI. Chinese Journal of Biomedical Engineering, 2019, 38(1): 28-34.
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