Human brain MRI hippocampus detection and segmentation method based on deep learning

A technology of deep learning and hippocampus, applied in medical science, diagnostic recording/measurement, diagnostic signal processing, etc., can solve problems such as difficult and accurate segmentation for radiologists, achieve automatic and efficient detection and segmentation, improve effect, and reduce variance Effect

Inactive Publication Date: 2018-12-07
WUHAN UNIV OF SCI & TECH
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0004] In order to solve the above technical problems, the present invention provides a human brain MRI hippocampus detection and segmentation method based on dee

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  • Human brain MRI hippocampus detection and segmentation method based on deep learning
  • Human brain MRI hippocampus detection and segmentation method based on deep learning
  • Human brain MRI hippocampus detection and segmentation method based on deep learning

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Embodiment Construction

[0019] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0020] please see figure 1 A method for detecting and segmenting human brain MRI hippocampus based on deep learning provided by the invention, comprising the following steps:

[0021] Step 1: MRI data preprocessing;

[0022] The specific implementation includes the following sub-steps:

[0023] Step 1.1: Calculate the relative positional relationship between the hippocampus and the limbus of the brain in MRI, and obtain the largest bounding box of the hippocampus;

[0024] Step 1.2: Data preprocessing;

[0025] Step 1.2.1: Cut off the blank parts in three...

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Abstract

The invention discloses a human brain MRI hippocampus detection and segmentation method based on deep learning. The method includes the steps of firstly, conducting data pretreatment on human brain MRI data and labels both of which are obtained by means of various channels and creating a model; secondly, determining a final model and determining super parameters of the final model; thirdly, training and estimating the model; finally, predicating the model, comparing a model predication result with a manual segmentation result, observing the effect, and conducting analysis to obtain a final predicated image. According to the method, historical manual segmentation result image information is fully utilized, not only can detection and segmentation be automatically and efficiently carried out,but also convenience is provided for solving the problems of shortage of doctors in the image department, a poor primary medical capability, a great disparity in the proportion of doctors and patients and the like. When the model is subjected to fitting, L2 regular terms are added for the first time, and regular term super parameters are also added, correspondingly the variance of the model is reduced, and the effect is obviously improved. Through several experiments, the network depth is increased to five layers, and the effect of the model is also improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence auxiliary analysis, relates to the technical field of MRI image hippocampus detection and segmentation, and in particular to a human brain MRI hippocampus detection and segmentation method based on deep learning. Background technique [0002] Alzheimer's disease, also known as senile dementia, is a neurodegenerative disease with insidious onset, slow development, and progressive deterioration over time. There is often no exact onset time and onset symptoms, and it is often difficult to be discovered in the early stage. Once it occurs, it will slowly progress irreversibly. Studies have shown that senile dementia is the fourth leading cause of death in the elderly after tumors, heart disease, and cerebrovascular diseases, and its treatment costs rank third among all diseases. With the aging of my country's population, the number of patients with Alzheimer's disease continues to incr...

Claims

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Application Information

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IPC IPC(8): A61B5/055
CPCA61B5/055A61B5/72
Inventor 杨俊汪检兵李阳
Owner WUHAN UNIV OF SCI & TECH
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