Dense Dilated Inception Network for Medical Image Segmentation

Abstract

In recent years, various encoder-decoder-based U-Net architecture has shown remarkable performance in medical image segmentation. However, these encoder-decoder U-Net has a drawback in learning multi-scale features in complex segmentation tasks and weak ability to generalize to other tasks. This paper proposed a generalize encoder-decoder model called dense dilated inception network (DDI-Net) for medical image segmentation by modifying U-Net architecture. We utilize three steps; firstly, we propose a dense path to replace the skip connection in the middle of the encoder and decoder to make the model deeper. Secondly, we replace the U-Net's basic convolution blocks with a modified inception module called multi-scale dilated inception module (MDI) to make the model wider without gradient vanish and with fewer parameters. Thirdly, data augmentation and normalization are applied to the training data to improve the model generalization. We evaluated the proposed model on three subtasks of the medical segmentation decathlon challenge. The experiment results prove that DDI-Net achieves superior performance than the compared methods with a Dice score of 0.82, 0.68, and 0.79 in brain tumor segmentation for edema, non-enhancing, and enhancing tumor. For the hippocampus segmentation, the result achieves 0.92 and 0.90 for anterior and posterior, respectively. For the heart segmentation, the method achieves 0.95 for the left atrial.