Unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning

Abstract

The invention provides an unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning. The method comprises the steps of deep coding-decoding full-convolution network segmentation system model setup, domain discriminator network model setup, segmentation system pre-training and parameter optimization, adversarial training and target domain feature extractor parameter optimization and target domain MRI brain tumor automatic semantic segmentation. According to the method, high-level semantic features and low-level detailed features are utilized to jointly predict pixel tags by the adoption of a deep coding-decoding full-convolution network modeling segmentation system, a domain discriminator network is adopted to guide a segmentation model to learn domain-invariable features and a strong generalization segmentation function through adversarial learning, a data distribution difference between a source domain and a target domain is minimized indirectly, and a learned segmentation system has the same segmentation precision in the target domain as in the source domain. Therefore, the cross-domain generalization performance of the MRI brain tumor full-automatic semantic segmentation method is improved, and unsupervised cross-domain adaptive MRI brain tumor precise segmentation is realized.

Description

technical field [0001] The invention relates to the technical field of medical image analysis, in particular to an unsupervised domain adaptive multimodal MRI brain tumor semantic segmentation method based on deep confrontational learning. Background technique [0002] Brain tumors, especially gliomas, grow rapidly and are highly destructive. Because they are easy to damage the central nervous system of the human brain, the fatality rate is very high. Early detection and precise treatment of brain tumors will help improve the cure rate and survival period. Magnetic Resonance Imaging (MRI) has become the preferred tool for clinical brain tumor detection and diagnosis due to its high-resolution and multi-imaging protocols. Provide imaging information. Identifying brain tumors from MRI images and accurately segmenting tumor regions and intratumoral structures is of great clinical significance. Accurate segmentation of brain tumors provides important support for neuropatholog...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems of slow segmentation speed and limited segmentation accuracy of existing MRI brain tumors, and the segmentation performance drops significantly in the case of domain shift, the present invention provides an unsupervised domain-adaptive semantic segmentation method for brain tumors based on deep adversarial learning , the method uses a deep encoder-decoder fully convolutional network to model a segmentation system, uses high-level semantic features and low-level detail features to jointly predict pixel labels, and uses a domain discriminator network to guide the segmentation model to learn domain-invariant features and strong generic features through adversarial learning. In an indirect way, the data distribution difference between the source domain and the target domain is minimized, so that the learned segmentation system has the same segmentation accuracy as the source domain in the target domain, thereby improving the cross-domain generality of the automatic semantic segmentation method for MRI brain tumors. It realizes accurate segmentation of unsupervised cross-domain adaptive MRI brain tumors, and solves the problem of segmentation system learning in the context of unlabeled data samples in the target domain.

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