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| Medical image segmentation is a very vital research field in computer vision. How to realize an instant and accurate segmentation is of great importance in medical image segmentation. Image segmentation based on deep learning technique can be described as an encoder-decoder architecture. The most classic existed encoder-decoder model is U-Net. However, it can not solve the blurred boundary problem in predicting the segmentation result of the high resolution image. Therefore, this paper proposes a deep learning method that is based on boundary information. This paper proposes adopting Octave convolution to decompose the features into low-frequency feature and high-frequency feature and utilizing the low spatial frequency component to get the segmentation of the smoothly changing structure in the original image and the high spatial frequency component to get the segmentation of the rapidly changing fine details in the original image, followed by using the segmentation of fine details as the constrain condition. This paper proposes concatenating the smoothly changing structure segmentation and the rapidly changing fine details segmentation to realize the constrain condition. The segmentation result of the whole original image is obtained by putting the concatenated segmentation into the convolutional layer for class prediction. Meanwhile, this paper considers the class imbalance problem in the multi-class segmentation and proposes giving more weight to the rare classes. Because this paper adopts Octave convolution and the encoder-decoder method as U-Net, this paper calls the proposed approach Oct-UNet. This proposed method can not only achieve better results than U-Net, but also contains less parameters. The following conducted experiments verify the effect of the proposed approach. |
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| Keywords:Image processing; Image segmentation; Boundary information;Class imbalance |
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