Kidney segmentation method in CT image based on deep learning

Abstract

The invention discloses a kidney segmentation method in a CT image based on deep learning. The method comprises the following steps: inputting a CT image group; carrying out normalization processing on each image in the image group; generating a position coding graph of the kidney, and superposing the position coding graph with each image; carrying out convolution on all the images, determining aninterested position area, carrying out pixel Hadamard code product on all the processed images, and obtaining segmented images; binarizing the image at the output end; and carrying out Hadamard codeproduct on the obtained image and each image after normalization processing, and determining and outputting the image of which the kidney part is segmented. According to the method, the position codesare added when the neural network is used for training the images, so that the problem that the spleen and the kidney are difficult to distinguish can be well distinguished and solved; besides, an attention mechanism is used, so that the network fitting convergence speed is higher, the network can pay attention to the kidney position area, and therefore, spleen interference is eliminated.

Description

technical field [0001] The invention relates to the technical field of image segmentation, in particular to a method for segmenting kidneys in CT images based on deep learning. Background technique [0002] With the large-scale growth of image data on the Internet, image segmentation technology has been widely concerned and applied. Especially in the segmentation of the medical field, the cost of segmentation for doctors is high, the efficiency of segmentation is low, and the segmentation standards are uneven. [0003] Existing medical segmentation technology is generally based on traditional computer vision technology, project CT value (-1000Hu, 1000Hu) to RGB space (0-255), manually extract features, use heuristic algorithm according to doctor's experience, use the extracted Features such as dimensionality reduction and machine learning algorithms. Such a method has many advantages. For example, the experience of doctors can be well automated. For example, a series of ma...

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Problems solved by technology

[0005] For example, the neural network Unet does have a certain effect on segmentation, but for images with similar shapes and similar CT values, such as figure 1 As shown, the kidney and spleen will be confused. In view of this, it is urgent to provide a method that can accurately identify and segment the kidney in CT images.

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