Abstract: A medical image segmentation method include: 1) acquiring a medical image data set; 2) acquiring, from the medical image data set, an original image and a real segmentation image of a target region in the original image in pair to serve as an input data set of a pre-built constant-scaling segmentation network, the input data set including a training set, a verification set, and a test set; 3) training the constant-scaling segmentation network by using the training set to obtain a trained segmentation network model, and verifying the constant-scaling segmentation network by using the verification set, the constant-scaling segmentation network including a feature extraction module and a resolution amplifying module; and 4) inputting the original image to be segmented into the segmentation network model for segmentation to obtain a real segmentation image.

Abstract: The present disclosure discloses an image segmentation method combined with superpixel and multi-scale hierarchical feature recognition. This method is based on a convolutional neural network model taking multi-scale hierarchical features extracted from a Gaussian pyramid of an image as a recognition basis, and then being connected with a multilayer perceptron to achieve the recognition of each pixel in the image, moreover, this method is used tier performing superpixel segmentation on the image and is combined with a method for improving superpxiel in combination with LBP texture features to segment an original image so that an obtained superpixel block is more fitted to edges of targets, then, the original image is merged according to a mean value of a color, and finally, recognition of each target in the image is achieved.

Abstract: A medical image segmentation method include: 1) acquiring a medical image data set; 2) acquiring, from the medical image data set, an original image and a real segmentation image of a target region in the original image in pair to serve as an input data set of a pre-built constant-scaling segmentation network, the input data set including a training set, a verification set, and a test set; 3) training the constant-scaling segmentation network by using the training set to obtain a trained segmentation network model, and verifying the constant-scaling segmentation network by using the verification set, the constant-scaling segmentation network including a feature extraction module and a resolution amplifying module; and 4) inputting the original image to be segmented into the segmentation network model for segmentation to obtain a real segmentation image.