DEU-Net: Dual Encoder U-Net for 3D Medical Image Segmentation

Abstract

As medical image analysis equipment has evolved and gained popularity, MRI has taken the forefront in radiological imaging. Since 3D medical images are more complex and contextual features are more difficult to capture, Transformer needs to be introduced to enhance the feature extraction capabilities of the network. We propose Dual Encoder U-Net (DEU-Net), which uses Transformer and CNN to extract medical image features in the encoder. Transformer is a pre-trained model in BTCV, which improves its ability to capture contextual features of medical images and increases the learning speed. To fuse the two kinds of features, we propose a Dual Feature Fusion Module (DFFM) to fuse the features extracted from the Transformer and CNN respectively, making full use of the feature extraction capabilities of the two extractors for 3D medical image. The results demonstrate that DEU-Net outperforms state-of-the-art for three segmentation tasks on the BraTS 2020 dataset.