Deep learning segmentation

Based on FCN, Ronneberger et al. [33] designed a U-Net network for biomedical images, which was widely used in medical image segmentation after it was proposed. Due to its excellent performance, U-Net and its variants have been widely used in various sub-fields of computer vision (CV). This approach was presented at the 2015 MICCAI conference and has been cited more than 4000 times. So far, U-Net has had many variants. There are many new design methods of convolutional neural network. But many of them still cited the core idea of U-Net, adding new modules or integrating other design concepts. With emerging of the end-to-end FCN, Ronneberger et al. [], using the idea of the FCN, proposed a U-shape Net (U-Net) framework for biomedical image segmentation. Ultimately, the convolution layer draws the attribute vector to the number of classes required at the final partitioning output. The U-Net model has some advantages compared to other patch-based segmentation approaches []: (1) It works well with very few training data. (2) It can utilize the global location and context information simultaneously. (3) It ensures maintenance of the complete texture of the input images. U-Net network is composed of U channel and skip-connection. The U channel is similar to the encoder-decoder structure of SegNet. The encoder has four submodules, each of which contains two convolutional layers. After each submodule, there is a max pool to realize downsampling. The decoder contains four submodules. The resolution is increased successively by upsampling. Then it gives predictions for each pixel. The network structure is shown in Figure 4. The input is 572 × 572, and the output is 388 × 388. The output is smaller than the input mainly because of the need for segmentation in the medical field, which is more accurate. It can be seen from the figure that this network has no fully connected layer, only convolution and downsampling. The network also uses a skip connection to connect the upsampling result to the output of submodule with the same resolution in the encoder as the input of next submodule in the decoder. Figure 4. The structure of the U-Net [33]. The reason why U-Net is suitable for medical image segmentation is that its structure can simultaneously combine low-level and high-level information. The low-level information helps to improve accuracy. The high-level information helps to extract complex features.