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A three-dimensional segmentation method of brain MRI hippocampus based on depth learning

A technology of deep learning and hippocampus, applied in the fields of machine learning and computer vision, can solve problems such as low contrast, unclear boundaries, and difficult to accurately segment, and achieve the effect of shortening the segmentation time, increasing the richness, and high segmentation accuracy

Active Publication Date: 2019-01-15
JIANGNAN UNIV
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AI Technical Summary

Problems solved by technology

However, the size of the hippocampus is small, the shape is irregular and varies from person to person, and the contrast with the surrounding tissue structure is low under conventional MRI images, and the boundary is unclear or even discontinuous
It is difficult for radiologists with many years of clinical experience to perform accurate segmentation
[0003] However, the ratio of doctors to patients in my country is extremely disparate, and the scarce doctor resources are far from being able to meet the needs of the huge patient population.
Moreover, the medical strength of grass-roots hospitals is weak, and the level of doctors is uneven, causing a large number of patients to flock to large tertiary hospitals, further exacerbating the imbalance of doctor-patient ratio

Method used

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  • A three-dimensional segmentation method of brain MRI hippocampus based on depth learning
  • A three-dimensional segmentation method of brain MRI hippocampus based on depth learning
  • A three-dimensional segmentation method of brain MRI hippocampus based on depth learning

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Embodiment Construction

[0046] Specific embodiments of the present invention will be described in detail below in combination with technical solutions and accompanying drawings.

[0047] Such as figure 2 As shown, the network structure of the present invention mainly combines FCN, U-Net 3D and convolutional neural network CNN.

[0048] U-Net is a semantic segmentation network based on FCN. In the U-Net structure, down-sampling and up-sampling are combined, bottom-level information and high-level information are combined, and the bottom-level features (same resolution cascade) are used to improve the lack of up-sampling information. Significantly improve the accuracy of segmentation. However, medical image data is generally less, and the underlying features are still important. Compared with ordinary images, medical images have very high complexity, large gray scale range, and unclear boundaries, so the U-Net structure is very suitable. U-Net technology is used for medical image segmentation, such ...

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Abstract

The invention relates to the field of computer vision and machine learning, in particular to a three-dimensional segmentation method of brain MRI hippocampus based on depth learning. 2, building a network model; the three-dimensional segmentation network model of brain MRI hippocampus consists of three one-dimensional convolution layers, 15 three-dimensional convolution layers and 4 maximal cisternization layers. The whole network model is divided into two parts: left side and right side. The contraction path on the left side is used to capture the content of the image, and the expansion pathon the right side is used to accurately locate the image. To the left is a down-sampling process, divided into five sets of convolution operations. 3, training the model; the normalized image set E isused as the training set to train the three-dimensional segmentation network model of the brain MRI hippocampus in step 2, and the trained network model F for the three-dimensional segmentation of the brain MRI hippocampus is obtained. The invention ensures high division precision, and has high operation speed and expansibility.

Description

technical field [0001] The present invention relates to the fields of computer vision and machine learning, in particular to a three-dimensional segmentation method of brain MRI hippocampus based on deep learning. Background technique [0002] The early clinical manifestations of Alzheimer's disease (commonly known as senile dementia) are hippocampal atrophy. Doctors can perform three-dimensional imaging of the patient's brain through nuclear magnetic resonance technology, and then conduct diagnosis and design related treatment plans based on image analysis. When judging whether the hippocampus is shrinking, doctors usually need to segment the hippocampal structure and perform shape and volume analysis. However, the size of the hippocampus is small, the shape is irregular and varies from person to person, and under conventional MRI images, the contrast with the surrounding tissue structure is low, and the boundary is unclear or even discontinuous. It is difficult for radiol...

Claims

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Application Information

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IPC IPC(8): G06T7/11
CPCG06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30016G06T7/11
Inventor 肖志勇刘辰刘徐吴鑫鑫
Owner JIANGNAN UNIV
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