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Semi-supervised medical image segmentation method based on adversarial collaborative training

A medical image, collaborative training technology, applied in medical images, healthcare informatics, instruments, etc., can solve problems such as limited pre-training model performance, achieve good universality and versatility, and improve segmentation performance.

Active Publication Date: 2019-08-06
NANJING UNIV
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Problems solved by technology

[0004] However, self-training-based methods are limited by the performance of pre-trained models

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  • Semi-supervised medical image segmentation method based on adversarial collaborative training
  • Semi-supervised medical image segmentation method based on adversarial collaborative training
  • Semi-supervised medical image segmentation method based on adversarial collaborative training

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Embodiment Construction

[0041] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0042] A semi-supervised segmentation method for medical images based on adversarial collaborative training. The schematic diagram of the network structure is as follows: figure 1shown. The network consists of two sub-networks: a segmentation network and a discriminator. The split network uses two encoder-decoder architectures and shares their encoder parts. The two decoder branches can use a co-training approach to supervise each other to improve performance. The discriminator uses a conventio...

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Abstract

The invention discloses a semi-supervised medical image segmentation method based on adversarial collaborative training. A neural network segmentation model is trained by using a small amount of labeled medical image data and a large amount of unlabeled medical image data, so that the model performance is improved. The model uses two decoder branches with different structures, the two decoder branches share the same encoder, and the two decoder branches can learn each other through a cooperative training method. Meanwhile, the model also uses an adversarial learning method to train a discriminator, and the discriminator can learn the high-order continuity between the segmentation result and the real label, so that the output of the segmentation network is closer to the real label visually.And meanwhile, the discriminator can also select the part with higher confidence in the unlabeled data pseudo tags to train the segmentation model. The method provided by the invention is not limitedby diseases and focus types, can be used for medical image segmentation of diseases of various parts such as the liver and the oral cavity, and has very good universality and universality.

Description

technical field [0001] The invention relates to a semi-supervised medical image segmentation method based on confrontational collaborative training, which is suitable for medical image data sets with less labeled data and more unlabeled data. Moreover, the method of the present invention is not limited by the type of disease, and can be applied to medical image segmentation of diseases in various parts such as liver and oral cavity, and has universality and universality. Background technique [0002] Semantic segmentation is a very important task in medical image analysis. It can detect physiological structures and the location and size of lesion areas, and help formulate medical plans. With the development of deep learning, deep neural networks, especially fully convolutional networks, have shown good performance in segmenting natural scene pictures and medical images. However, the current mainstream neural network structures contain a large number of parameters that need ...

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Application Information

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IPC IPC(8): G06K9/62G16H30/40
CPCG16H30/40G06F18/2155G06F18/214Y02T10/40
Inventor 李武军房康陈龙意周嵩
Owner NANJING UNIV
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