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Quick fetal MR image brain extraction method based on multi-scale neural network

A neural network and extraction method technology, applied in the field of rapid fetal MR image brain extraction, can solve the problems of poor imaging effect, thick scanning layer, etc., and achieve the effect of fast and accurate brain extraction

Pending Publication Date: 2020-04-28
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0005] The present invention provides a fast fetal MR image brain extraction method based on a multi-scale neural network to solve the problems of poor imaging effect and large scanning layer thickness caused by pregnant women's breathing and fetal movement during fetal brain MR scanning in the prior art

Method used

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  • Quick fetal MR image brain extraction method based on multi-scale neural network
  • Quick fetal MR image brain extraction method based on multi-scale neural network
  • Quick fetal MR image brain extraction method based on multi-scale neural network

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Embodiment Construction

[0023] Below in conjunction with embodiment the present invention will be further described.

[0024] The experimental data in this specific embodiment are all from real data sets of MR scans of pregnant women's abdomen.

[0025] Such as figure 1 As shown, the network structure of the fast fetal MR image brain extraction method based on the multi-scale neural network mentioned in the present invention is as follows:

[0026] The network model proposed in the present invention is an end-to-end model. The input is an MR image and the output is the extracted brain result. In the encoding process of the network, the multi-scale feature extraction module is first used to extract multi-scale features from the input image, and then the extracted multi-scale features are fused using the attention-based feature fusion module. Then use a maximum pooling with a kernel of 2×2×2 and a stride of 1 to obtain more advanced texture features of the image. Then repeat the above process to ob...

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Abstract

The invention discloses a quick fetal MR image brain extraction method based on a multi-scale neural network, and belongs to the technical field of medical images. The method comprises the following steps: carrying out the multi-scale learning of fetal MR image features; fusing the fetal MR image multi-scale features by using a channel attention model; performing brain extraction on the fetal MR image based on the multi-scale features; according to the method, the MR image containing the fetal brain is quickly extracted by using the full convolutional network, and the extracted multi-scale features are fused by using a channel attention mode, so that an accurate fetal brain extraction result is obtained.

Description

technical field [0001] The invention belongs to the technical field of medical images, in particular to a method for rapidly extracting brains from fetal MR images based on a multi-scale neural network. Background technique [0002] With the rapid development of the information age, machine learning and deep learning methods have received more and more attention. The machine learning method learns the distribution of the data by learning the data, so as to classify the data. In particular, in the past five years, deep learning methods based on convolutional neural networks have been widely used in medical image segmentation, tissue localization, and disease-aided diagnosis. [0003] The convolutional neural network can learn data and task-driven image features, so as to better represent the features of the image. In particular, the end-to-end fully convolutional neural network can quickly extract, locate, and segment images. A fully convolutional neural network contains o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10088G06T2207/30016G06T2207/20084G06T2207/20221
Inventor 张道强孙亮张俊艺
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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