GSDNet: An Anti-interference Cochlea Segmentation Model Based on GAN

Abstract

Medical segmentation of cochlear images aims to identify the area of the cochlea in a set of CT slices. The shape of cochlea will vary a quite in different CT slicing levels, and the relevant dataset has a higher labeling cost. This will lead to segmentation results with edge discontinuity when we implement supervised algorithm under few samples. In order to solve the problem of a small number of labeled images, this paper proposes a semi-supervised model called GSDNet which is based on GAN, which captures the features of the cochlear image without labels, so as to achieve high performance for processing fewer sampled data. To further improve the generalization of the model, we adopt a training method that allows the model to gradually distinguish between real images and fake images. In addition, in order to solve the problem of local noise interference and discontinuous segmentation results, we introduce a label discrimination network to force the distribution of generated results from segmentation network to align with the true label distribution, so that the edges of the segmentation results are continuous and the shape is more accurate. Finally, we conduct a segmentation experiment of the cochlear region containing 30 slices about cochlea data, and compare different cutting-edge methods. The method proposed in this paper achieves higher performance on the dice index.