Metadata about: Medical image segmentation is a fundamental and challenging problem for analyzing medical images due to the approximate pixel values of adjacent tissues in boundary and the non-linear feature between pixels. Although fully convolutional neural networks such as U-Net has demonstrated impressive performance on medical image segmentation, distinguishing subtle features between different categories after pooling layers is still a difficult task, which affects the segmentation accuracy. In this paper, we propose a Mini-Inception-Residual-Dense (MIRD) network named MIRD-Net to deal with this problem. The key point of our proposed MIRD-Net is MIRD Block. It takes advantage of Inception, Residual Block (RB) and Dense Block (DB), aiming to make the network obtain more features to help improve the segmentation accuracy. There is no pooling layer in MIRD-Net. Such a design avoids loss of information during forward propagation. Experimental results show that our framework significantly outperforms U-Net in six different image segmentation tasks and its parameters are only about 1/50 of U-Net.

About: Medical image segmentation is a fundamental and challenging problem for analyzing medical images due to the approximate pixel values of adjacent tissues in boundary and the non-linear feature between pixels. Although fully convolutional neural networks such as U-Net has demonstrated impressive performance on medical image segmentation, distinguishing subtle features between different categories after pooling layers is still a difficult task, which affects the segmentation accuracy. In this paper, we propose a Mini-Inception-Residual-Dense (MIRD) network named MIRD-Net to deal with this problem. The key point of our proposed MIRD-Net is MIRD Block. It takes advantage of Inception, Residual Block (RB) and Dense Block (DB), aiming to make the network obtain more features to help improve the segmentation accuracy. There is no pooling layer in MIRD-Net. Such a design avoids loss of information during forward propagation. Experimental results show that our framework significantly outperforms U-Net in six different image segmentation tasks and its parameters are only about 1/50 of U-Net.

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