Medical image processing method and device, electronic equipment and computer storage medium

Abstract

The embodiment of the invention provides a medical image processing method and device, electronic equipment and a computer storage medium. The method can comprise the steps of obtaining a to-be-processed 3D medical image; segmenting the 3D medical image into at least two adjacent image blocks; performing feature extraction on each image block to obtain a feature map of each image block; determining a prediction probability graph corresponding to each image block based on the respective feature graph of each image block; and based on the prediction probability graph of each image block, determining a detection result of a lesion area corresponding to each disease in the 3D medical image. Through the scheme of the invention, the detection result corresponding to the same disease in the 3D medical image can be accurately determined based on the image blocks and the relevance among the image blocks.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular, the present invention relates to a medical image processing method, device, electronic equipment and computer storage medium. Background technique [0002] In order to detect the location of the lesion in the 3D medical image, the existing medical image processing method is: based on the 2D or 3D neural network model, the 3D medical image is detected to determine whether there is a lesion in the medical image, and whether the lesion is in the image s position. However, due to the large amount of data of 3D medical images, when processing the 3D medical images based on the 2D neural network model, the detection effect will be unsatisfactory. When processing the 3D medical images based on the 3D neural network model, due to the large amount of data , and the number of parameters of the model is also large, which leads to an excessive amount of calculation of the alg...

AI Technical Summary

Problems solved by technology

However, due to the large amount of data of 3D medical images, when processing the 3D medical images based on the 2D neural network model, the detection effect will be unsatisfactory. When processing the 3D medical images based on the 3D neural network model, due to the large amount of data , and the number of parameters of the model is also large, resulting in an excessive amount of calculation of the algorithm and affecting the performance of the model

Therefore, the algorithms in the prior art have poor performance and cannot meet the processing requirements for 3D medical images.

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