Medical image segmentation method based on dual-path U-shaped convolutional neural network

A convolutional neural network and medical image technology, applied in the field of medical image segmentation, can solve problems such as cumbersome process, non-existence of successful segmentation, and failure to achieve fully automatic segmentation efficiency and accuracy, so as to improve training accuracy and convergence Effects of speed, good performance

Inactive Publication Date: 2018-09-04
CHENGDU UNIV
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Problems solved by technology

[0003] Traditional image segmentation technology, whether it is manual segmentation or semi-automatic segmentation, requires manual input, the process is cumbersome, and largely depends on the experience and knowledge of the operator, and the segmentation results are difficult to reproduce, and fully automatic seg

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  • Medical image segmentation method based on dual-path U-shaped convolutional neural network
  • Medical image segmentation method based on dual-path U-shaped convolutional neural network
  • Medical image segmentation method based on dual-path U-shaped convolutional neural network

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[0061] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the implementations shown and described in the drawings are only exemplary, intended to explain the principle and spirit of the present invention, rather than limit the scope of the present invention.

[0062] Embodiments of the present invention provide a medical image segmentation method based on a dual-channel U-shaped convolutional neural network, such as figure 1 As shown, including the following steps S1-S4:

[0063] S1. Perform preprocessing on functional magnetic resonance image (MRI) data to be segmented to obtain training set data and test set data.

[0064] like figure 2 As shown, step S1 includes the following sub-steps S11-S14:

[0065] S11. Perform format conversion on the fMRI image data to be segmented.

[0066] S12. Perform normalization processing on the format-converted image, and normalize it to...

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Abstract

The present invention discloses a medical image segmentation method based on a dual-path U-shaped convolutional neural network. A dual-path module and a U-shaped network are combined, original features are reused through stacking operation, and features are added to generate new features so as to improve the network training precision; the U-shaped convolutional neural network configuration is employed to perform transverse feature stacking between blocks so as to greatly improve the network training convergence speed and more rapidly obtain an ideal training model. The medical image segmentation method can meet the demands of data size of medical images requiring being processed is large and high processing efficiency and high precision requirements and can achieve a very good performancein the biomedicine detection application.

Description

technical field [0001] The invention belongs to the technical field of medical image segmentation, and in particular relates to the design of a medical image segmentation method based on a dual-channel U-shaped convolutional neural network. Background technique [0002] Medical image segmentation includes manual segmentation, semi-automatic segmentation and fully automatic segmentation. Manual segmentation is difficult and the process is cumbersome. Although the accuracy is high, it largely depends on the experience and knowledge of the operator, and the segmentation results are difficult to reproduce; semi-automatic segmentation It is an interactive method that combines manual and computer processing, which allows human interactive operation to provide some useful information, and then the computer performs segmentation and processing. Semi-automatic segmentation also includes methods such as Gragh Cut (graph cut), CRF (conditional random field) and level set (level set). T...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06N3/04G06N3/08
CPCG06N3/08G06T7/0012G06T7/10G06T2207/20081G06T2207/20084G06T2207/10088G06T2207/30004G06N3/045
Inventor 于曦胡科刘昶何煜朱泓超
Owner CHENGDU UNIV
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