Methods, systems, and medium for generating registered multi-modal MRI with lesion segmentation tags

Abstract

The invention discloses a method, a system and a medium for generating registered multi-modal MRI with focus segmentation tags. The method comprises the following steps: acquiring a random matrix in normal distribution, inputting the random matrix into a trained structural feature decoder in a generative adversarial network, and decoding to generate a structural feature map; fusing the structuralfeature map and the randomly selected focus segmentation label map through random input to obtain a fusion result, and inputting the fusion result into a trained random encoder in the generative adversarial network to obtain a code; and inputting the code into a decoder of each mode trained in the generative adversarial network, and respectively generating registered multi-mode MRI. The generatorin the generative adversarial network is modularized into the encoders and the decoders, through combined training of the encoders, the decoders and the discriminators, random input conforming to design specifications can be received, then a set of registered multi-mode MRI images with focus segmentation tags are generated, and the method can be widely applied to the field of medical images.

Description

technical field [0001] The present invention relates to image generation technology in the medical field, in particular to a method, system and medium for generating a registered multimodal MRI with a lesion segmentation label, which is used to obtain a registered image according to a given random input that meets the specification Multimodal MRI images with lesion segmentation labels. Background technique [0002] With the development of deep learning, more and more fields have begun to use deep neural networks for image processing tasks. However, the training of deep neural networks requires a large amount of data, but in many fields, the construction of data sets is very difficult. Therefore, image generation technology has important uses in image intelligent processing scenarios in many fields, such as medical imaging and biological cell imaging. In the intelligent processing of medical images, medical images have many modalities, such as magnetic resonance imaging (MR...

AI Technical Summary

Problems solved by technology

However, medical image collection is very difficult, especially for rare diseases, making medical image datasets scarce and small, which makes many training tasks impossible

Naturally, registered multimodal image data is even scarcer

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