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Brain image segmentation method based on deep learning

An image segmentation and deep learning technology, applied in neural learning methods, image analysis, image enhancement, etc., can solve problems such as blurred edges of segmented images, achieve high segmentation accuracy and efficiency, solve poor segmentation effects, and solve network training gradients Diffusion effect

Pending Publication Date: 2021-05-11
DALIAN NATIONALITIES UNIVERSITY
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Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention provides a brain image segmentation method based on deep learning, which improves the network model's ability to obtain image detail information, reduces the computational complexity of model training, and solves the blurred edges of segmented images. problem, improved segmentation accuracy

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  • Brain image segmentation method based on deep learning
  • Brain image segmentation method based on deep learning
  • Brain image segmentation method based on deep learning

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Embodiment 1

[0030] A brain image segmentation method based on deep learning, comprising:

[0031] S1: Obtain the original brain image dataset;

[0032] Brain MRI image data and segmented hippocampal label image data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI for short) library. The above image data included real patients and healthy comparison groups, and the data format was nifti.

[0033] S2: Preprocessing the acquired original brain image dataset;

[0034] The images in the brain image data set are rotated, mirrored, flipped, and color dithered to enhance processing, cropped, and the image resolution is adjusted, and the brain image data set is divided into a training set and a test set according to the ratio of 8:2.

[0035] S3: Import the preprocessed brain image dataset into the brain image segmentation model for training, use the trained and mature brain image segmentation model to segment the brain image, and finally obtain the segmentation result; ...

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Abstract

The invention discloses a brain image segmentation method based on deep learning, and relates to the technical field of medical image processing and computer vision. The method comprises the steps: S1, obtaining an original brain image data set; s2, preprocessing the obtained original brain image data set; S3, importing the preprocessed brain image data set into a brain image segmentation model for training, and segmenting the brain image by using the brain image segmentation model which is maturely trained to finally obtain a segmentation result. The method improves the capability of obtaining image detail information of a network model, reduces the operation complexity of model training, solves the problem of edge blurring of segmented images, and improves the segmentation precision.

Description

technical field [0001] The invention relates to the technical fields of medical image processing and computer vision, in particular to a brain image segmentation method based on deep learning. Background technique [0002] The brain is the central organ of human physiological activities and thinking and emotion, and has a very complex structure and function. When the corpus callosum, thalamus, and hippocampus in the brain are damaged, it may cause brain diseases such as idiopathic normal pressure hydrocephalus and Alzheimer's disease. With the continuous development of artificial intelligence and computer technology, as well as the advancement of modern medical imaging technology, medical imaging technology is widely used in the diagnosis and treatment of various diseases. Doctors can use magnetic resonance imaging (MRI) technology to diagnose patients. . However, for brain images, the patient's diseased tissue may only be a small part or area of ​​the human brain, which i...

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Application Information

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IPC IPC(8): G06T7/00G06T7/12G06T5/00G06K9/46G06N3/04G06N3/08
CPCG06T7/0012G06T7/12G06T5/00G06N3/08G06T2207/10024G06T2207/20081G06T2207/30016G06V10/44G06N3/045
Inventor 张秀峰牛选兵杨荣锦龚莉娜
Owner DALIAN NATIONALITIES UNIVERSITY
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