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Extraction method of hippocampus from human brain MRI images based on 3D neural network

A technology of nuclear magnetic resonance and neural network, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of low precision and long time consumption, and achieve enhanced discrimination ability, reduced time, efficient automatic and accurate segmentation Effect

Active Publication Date: 2022-06-07
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a method for extracting the hippocampus of a human brain MRI image based on a 3D neural network, aiming to solve the time-consuming and excessive automatic segmentation technology of the hippocampus in the existing human brain MRI image. The problem of long length and low precision

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  • Extraction method of hippocampus from human brain MRI images based on 3D neural network
  • Extraction method of hippocampus from human brain MRI images based on 3D neural network
  • Extraction method of hippocampus from human brain MRI images based on 3D neural network

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[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0034] refer to figure 1 , the embodiment of the present invention provides a hippocampus extraction method based on a 3D neural network MRI of the human brain, including the following steps S1 to S5:

[0035] S1. Obtain the original image data set of human brain MRI images containing 3D labels as a training set, and preprocess the original image data set and labels;

[0036] In this embodiment, the acquired original image data set includes 130 groups of brain MRI hippocampus image files with a size of 197*233*189 in NIFTI format.

[0037] The acquired original image data set is preproce...

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Abstract

The invention discloses a method for extracting the hippocampus of a human brain MRI image based on a 3D neural network. The method includes preprocessing the original image data set and labels, constructing a 3D hippocampus segmentation neural network model, and defining a boundary enhancement loss function. , optimize the boundary enhancement loss function, use the trained 3D hippocampus segmentation neural network model to detect the preprocessed image to be detected, and obtain the hippocampus extraction result. The present invention utilizes a 3D hippocampus segmentation neural network model to realize efficient automatic and precise segmentation of the hippocampal structure in human brain MRI, and can reduce the time for doctors to diagnose Alzheimer's disease early.

Description

technical field [0001] The invention belongs to the technical field of hippocampus segmentation, and in particular relates to a hippocampus extraction method based on 3D neural network MRI of human brain. Background technique [0002] The hippocampal structure is an important tissue structure in the human brain, and its morphological analysis is crucial for the detection and diagnosis of clinical conditions in the brain. The structure of the hippocampus is related to memory mechanisms, and its morphological changes are closely related to Alzheimer's disease and other neurological diseases. Estimation of hippocampal atrophy from magnetic resonance images (MRI) is considered to be one of the key techniques for diagnosing Alzheimer's disease. However, manual segmentation of hippocampal structures in brain MRI is time-consuming, labor-intensive, and error-prone due to factors such as the small size and complex morphology of the hippocampus in the brain, and the indistinct bound...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/10G06N3/04G06N3/08
CPCG06T7/10G06N3/084G06T2207/10088G06T2207/20081G06T2207/20192G06T2207/30016G06N3/045
Inventor 和红杰颜宇陈帆
Owner SOUTHWEST JIAOTONG UNIV
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