Brain tissue layering method and device based on neural network, and computer equipment

Abstract

The embodiment of the invention belongs to the field of artificial intelligence, and relates to a brain tissue layering method and a device based on a neural network, computer equipment and a storagemedium. Extracting features of the brain CT image information through a pre-trained brain cutting convolutional neural network to obtain a feature map of the brain CT image; performing candidate box alignment operation on the feature map of the brain CT image to obtain semantic information of instance categories and instance pixel-level position information; and inputting the semantic informationof the instance category and the position information of the instance pixel level into a pre-trained layered neural network, and outputting a layering result of the brain CT image. By fusing the brainsegmentation convolutional neural network and the brain stratification neural network, the results of brain segmentation and brain stratification are obtained at the same time by using one model, sothat the operation time and the consumption of operation resources are reduced, and the two tasks of brain segmentation and brain stratification can share feature information, thereby improving the accuracy of brain stratification.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a neural network-based brain tissue layering method, device, computer equipment and storage medium. Background technique [0002] In recent years, deep learning technology has been widely used in various fields, especially computer vision, which is used to realize face recognition, target detection, image segmentation, etc. In the medical field, it is usually necessary to analyze brain CT images, which contain a lot of information. In addition to cerebral hemorrhage detection and brain tissue segmentation, even brain stratification is also a very important piece of information that needs to be provided. At present, the neural network in deep learning technology is generally used to build models to achieve detection, classification, prediction and other tasks, but usually multiple models are used to deal with multiple tasks, such as a detection model ...

AI Technical Summary

Problems solved by technology

At present, the neural network in deep learning technology is generally used to build models to achieve detection, classification, prediction and other tasks, but usually multiple models are used to deal with multiple tasks, such as a detection model for cerebral hemorrhage, brain tissue segmentation, etc. A segmentation model is produced, and a classification model is produced by brain layering. Such a strategy of multiple models to solve multiple tasks will double the calculation time and double the consumption of computing resources, and because the information of a single classification model is not comprehensive, the model will not be seen. misclassification of previous brain CT images, thereby reducing the accuracy of brain stratification

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