Liver CT image segmentation system and algorithm based on mixed supervised learning

A liver Computed Tomography (CT) image segmentation system based on mixed supervised learning, the image segmentation system comprising:

• an image preprocessing unit, a feature extraction unit, a word vector segmentation unit and a single-layer convolutional classification unit,
• the image preprocessing unit being in data connection with the feature extraction unit, and
• the feature extraction unit being respectively in data connection with the word vector segmentation unit and the single-layer convolutional classification unit;
• wherein a construction process of the feature extraction unit comprises:
• using U-Net as a basic framework, for an output feature map of a second-to-last convolutional layer of the U-Net, with a size satisfying (B, C0, H, W), where B represents a number of images in each batch, C represents a number of channels, and (H, W) represents a resolution of the output feature map,
• passing the output feature map through a convolutional layer with an input channel of C0, an output channel of C and a kernel size of (K0, K0), then
• performing batch normalization on an output result, and finally
• passing through a Tanh activation function to obtain a target feature map with a size of (B, C, H, W).