MR image placenta segmentation method of multi-task generative adversarial model

A multi-task, imaging technology, applied in biological neural network models, image analysis, image enhancement and other directions, can solve the problems of incomplete objects, easy to ignore interrelationships, and the segmentation accuracy needs to be improved, so as to meet clinical needs and strengthen adaptation. Ability, high segmentation accuracy effect

Pending Publication Date: 2021-07-23
NINGBO UNIV
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Problems solved by technology

However, the loss function of the traditional U-Net network regards each pixel in the image as independent of other pixels, and predicts the category of each pixel, which cannot reflect the connection between adjacent pixels and the relationship between pixels. Interrelationships are easily overlo

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  • MR image placenta segmentation method of multi-task generative adversarial model
  • MR image placenta segmentation method of multi-task generative adversarial model
  • MR image placenta segmentation method of multi-task generative adversarial model

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Embodiment 1

[0047] Example 1: A multi-task generating a MR image placenta segmentation method for generating a model, first build training sets and test sets, and then uses the generating counterfeit network to build a split model, then use the training set, test set, and division model. Training the segmentation model to obtain the divided model after training, finally dividing the placenta MR image by dividing the model to obtain the split picture of the placenta MR image, where the total counter loss function of the segmentation model is based on maximizing discriminant loss and minimizing more The task generation loss is constructed.

[0048] In this embodiment, the specific process of constructing training sets and test sets is:

[0049] Step 1-1, the MR image of the N-type resolution is 256 * 256, the number of the number 3 is 3, and the mask label corresponding to the N-MR image is obtained from the historical MR image database, and n is an integer equal to 1000. The same left and righ...

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Abstract

The invention discloses an MR image placenta segmentation method for a multi-task generative adversarial model. The method comprises the steps: constructing a training set and a test set, constructing a segmentation model through employing a generative adversarial network, training the segmentation model through employing the training set, the test set, and a total adversarial loss function of the segmentation model, and obtaining a trained segmentation model, finally, the placenta MR image is segmented through the segmentation model, segmented pictures of the placenta MR image are obtained, and a total adversarial loss function of the segmentation model is constructed based on maximization of discrimination loss and minimization of multi-task generation loss; the method has the advantages of being high in segmentation precision and capable of meeting clinical requirements.

Description

Technical field [0001] The present invention relates to a MR image placenta-division method, in particular, involving a MR image placenta segmentation method of a multi-task generating counterming model. Background technique [0002] In recent years, with the increase in the angels, the increasing abortion, the incidence of placenta implantation has increased the rise and has become one of the common clinical diseases in obstetric. The soft tissue resolution of magnetic resonance (MR) is high, and the image quality can be imagined in arbitrary direction, and the image quality is not affected by the fetal position, the maternal body type and the amount of water, and has gradually become an important to diagnose placental implantation. means. In clinical practice, precise segmentation of placental tissue is to identify placental implantation and assessment of implantation. By analysis of placenta and its surrounding tissue organs, it is expected to achieve placenta adhesion, implan...

Claims

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Application Information

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IPC IPC(8): G06T7/12G06T7/13G06T7/136G06N3/04
CPCG06T7/12G06T7/13G06T7/136G06T2207/10088G06T2207/20221G06N3/045Y02T10/40
Inventor 陈志远宣荣荣王玉涛方旭源金炜周阳涨
Owner NINGBO UNIV
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