Brain tumor image segmentation method based on depth learning and weight space integration

A deep learning and image segmentation technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as category imbalance, brain tumor segmentation image effect is rough, etc., to achieve accuracy improvement, image preprocessing operation is convenient, The effect of addressing class imbalance

Active Publication Date: 2019-01-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0006] In view of the above-mentioned deficiencies in the prior art, the brain tumor image segmentation method based on deep learning and weigh

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  • Brain tumor image segmentation method based on depth learning and weight space integration
  • Brain tumor image segmentation method based on depth learning and weight space integration
  • Brain tumor image segmentation method based on depth learning and weight space integration

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Embodiment Construction

[0043] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0044] Such as figure 1 As shown, the brain tumor image segmentation method based on deep learning and weight space integration includes the following steps,

[0045] S1. Acquiring multimodal MRI images of brain tumors, and performing preprocessing operations on the acquired multimodal MRI images;

[0046] In the above step S1, multimodal images of brain tumors are collected including Flair modality images, T1 modality ima...

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Abstract

The invention discloses a brain tumor image segmentation method based on depth learning and weight space integration, comprising the following steps: S1, acquiring a multimodal MRI image of the braintumor, and preprocessing the acquired multimodal MRI image; S2, constructing a first network model and a second network model; S3, inputting the preprocessed multimodal MRI image into the second network model, passing through the weight space, and updating the average weight; training it and storing the average value of weights into the first network model; S4, inputting the multimodal MRI image to be tested into the first network model, and outputting the segmentation result of the brain tumor image. The segmentation method of the invention is conducive to improving the accuracy and reliability of the brain tumor segmentation result, and the parallel network can obtain the positioning information while segmenting the lesion, and meanwhile, the method integrates the weight space collection, thereby improving the efficiency and the accuracy of the brain tumor segmentation.

Description

technical field [0001] The invention belongs to the technical field of medical image segmentation, and in particular relates to a brain tumor image segmentation method based on deep learning and weight space integration. Background technique [0002] Medical image segmentation is a key technology in image analysis and processing. Separating relevant tissues of interest according to the similarity and specificity of the image area is of great significance to the clinical diagnosis and treatment process and is the main premise of all follow-up work. , the quality of the segmentation effect will directly affect the smooth progress of the information processing work. Medical image segmentation of brain tumors is an important branch in the field of image segmentation. Brain tumor segmentation technology plays an important role in the clinical diagnosis and treatment of brain tumors. Through the segmentation results of brain tumors, doctors can measure tumor Size and location, su...

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Application Information

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IPC IPC(8): G06T7/11G06T7/00
CPCG06T7/0012G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30016G06T2207/30096G06T7/11
Inventor 程建郭桦周娇苏炎洲高银星
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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