An automatic tumor segmentation system and method, and an electronic device. The method comprises: acquiring an original image of a liver tumor, and respectively naming the original image and a corresponding ground truth image and then placing same in a folder (100); performing cutting processing on the original image, reserving a non-zero region of the image, analyzing data of the image and acquiring image data such as an interval, an intensity distribution and a shape, and placing the acquired image data in a new folder (200); constructing a MILU-Net network model by combining a U-Net network, an attention mechanism and a dual-stage cascade architecture (300); inputting data in the new folder into the MILU-Net network model for training, so as to obtain a weight file (400); and loading the weight file, configuring neurons of the MILU-Net network model according to the weight, and outputting a segmentation result (500). The present invention combines the features of network architectures having a plurality of segmentation network characteristics to overcome its own shortcomings by learning from the strong points of others, improving the existing monotonic network framework, improving the generalization performance and the segmentation precision of the model, and increasing the training efficiency.
AUTOMATIC TUMOR SEGMENTATION SYSTEM AND METHOD, AND ELECTRONIC DEVICE