Segmentation of Breast Tumors Based on Fully Convolutional Network and Dynamic Contrast Enhanced Magnetic Resonance Image

Abstract

Accurate and reliable breast tumor segmentation is essential for the diagnosis, treatment and prognosis of breast cancer. To address the shortcomings of existing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI)-based breast tumor segmentation methods, which tend to miss small tumors, we proposed a more reliable and efficient segmentation method for breast tumors in DCE-MRI based on a fully convolutional network (FCN). Firstly, the breast DCE-MRI data was preprocessed, followed by intercepting the image blocks of 128*128, and dividing the dataset into two sub-datasets according to the number of pixels in the tumor region. Secondly, the whole set was used to train CBP5-Net to obtain a classification model. Then, two sub-datasets were used to train RAU-Net to get two segmentation models. Finally, the test set was entered into the network, and the network outputs were post-processed to obtain the final segmentation results. The Dice coefficient, sensitivity, specificity and intersection over union (IoU) index of the method proposed in this paper reached 0.938 8, 0.952 3, 0.998 5 and 0.876 8, respectively. It proves that the proposed method can be used to segment DCE-MRI breast tumors effectively and accurately.

Keywords

• fully convolutional network (fcn)
• image segmentation
• breast tumor
• dynamic contrast enhanced-magnetic resonance imaging (dce-mri)