Multi-branch Context Awareness Network for Prostate MRI Segmentation

Abstract

Prostate image segmentation is a precondition for the diagnosis of prostate diseases and subsequent treatment. However, the blurry organ edges and low image contrast make accurate segmentation difficult. In order to overcome the difficulties, this paper proposes an innovative Multi-branch Context Awareness Network (MBCA-Net) for prostate MRI segmentation. MBCA-Net uses the 3D UNet as the backbone network with an encoder-decoder framework. To improve the feature extraction capacity of the network, a multi-branch residual module with different convolution kernels is proposed. To better make use of extracted features and reduce semantic ambiguity, a context awareness module is used to fuse low-level features in the encoder and high-level features in the decoder in a full process manner. To improve the stability of the model during training, a mixed loss function is adopted. Experiments on the public dataset PROMISE12 show that our model’s Dice coefficient is 90.19%. Compared with other advanced techniques, the proposed model has better segmentation results.