Medical image processing device and method using convolutional neural network

A convolutional neural network and image data technology, applied in the field of medical image processing devices, can solve problems such as difficult technical solutions, inability to use nuclear magnetic resonance imaging, huge computational load, etc., and achieve the effect of improving diagnostic efficiency

Pending Publication Date: 2020-03-27
罗亚川
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this document only processes the 3D voxel data of the MRI of the head, and the technical solution used is a 3D convolutional autoencoder
Since it is necessary to process all the 3-dimensional voxel data of the magnetic resonance imaging of each subject's head, this technical solution requires a huge amount of calculation, even if a high-performance computer or a high-perfo...

Method used

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  • Medical image processing device and method using convolutional neural network
  • Medical image processing device and method using convolutional neural network
  • Medical image processing device and method using convolutional neural network

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Embodiment approach 1

[0022] Embodiment 1 Utilizes the data of the Alzheimer's Disease Neuroimaging Initiative (ADNI for short) in the United States to realize the image processing and analysis of MRI in the present invention on the MRI image data of the cranium and the image data of the whole brain technical solutions. Download cranial MRIs of clinically confirmed Alzheimer's disease (AD), amnestic mild cognitive impairment (MCI) and normal aged group (NC) from the official website of the Alzheimer's Disease Neuroimaging Program in the United States image data,

[0023] Using the data of the Alzheimer's disease neuroimaging project in the United States, the 3T MRI image data of 450 subjects can be selected, and the training set, verification set and test set can be divided. The data in the training set is 313 subjects. 3T nuclear magnetic resonance image data, the data of the verification set is the 3T nuclear magnetic resonance image data of 120 subjects, and the 3T nuclear magnetic resonance im...

Embodiment approach 2

[0042] Embodiment 2 Utilizes the data of the Alzheimer's Disease Neuroimaging Initiative (ADNI for short) in the United States to realize the image processing and analysis of the MRI image data of the cranial MRI image data and the whole brain image data of the present invention technical solutions. Download cranial MRIs of clinically confirmed Alzheimer's disease (AD), amnestic mild cognitive impairment (MCI) and normal aged group (NC) from the official website of the Alzheimer's Disease Neuroimaging Program in the United States image data,

[0043] Using the data of the Alzheimer's disease neuroimaging project in the United States, the 3T MRI image data of 450 subjects can be selected, and the training set, verification set and test set can be divided. The data in the training set is 313 subjects. 3T nuclear magnetic resonance image data, the data of the verification set is the 3T nuclear magnetic resonance image data of 120 subjects, and the 3T nuclear magnetic resonance i...

Embodiment 3

[0062] The three-dimensional image data of the clinically confirmed normal elderly group, amnestic mild cognitive impairment and Alzheimer's disease were reconstructed through the hippocampus to the normal elderly group, amnestic mild cognitive impairment and Alzheimer's disease. Five to ten two-dimensional images near the most sensitive coronal part of the three classifications of Haimer's disease were used as training data. The three-dimensional image data of the clinically confirmed normal elderly group, amnestic mild cognitive impairment and Alzheimer's disease were reconstructed through the hippocampus to the normal elderly group, amnestic mild cognitive impairment and Alzheimer's disease. One or two two-dimensional images of the most sensitive coronal part of the three classifications of Haimer's disease were used as validation data and test data.

[0063] Preprocess the above image.

[0064] Use Caffe's open source deep learning computing framework to realize the progr...

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Abstract

The invention relates to a medical image processing device and method using a convolutional neural network. A data enhancement technology and a migration technology are utilized; supervised learning of a convolutional neural network for deep learning of artificial intelligence is achieved by using one to twenty two-dimensional slices passing through a coronal plane of a hippocampus of image data of nuclear magnetic resonance imaging of a three-dimensional skull of a subject; one or two two-dimensional images passing through the coronal plane of the hippocampus of the three-dimensional skull nuclear magnetic resonance imaging image data of the subject is selected as tested images so as to realize three-class distinguishing and identification of the normal senile group, the forgetting type mild cognitive impairment and the Alzheimer's disease.

Description

technical field [0001] The invention relates to an artificial intelligence medical image processing device and method. Processing and analysis of MRI image data of the whole brain and / or hippocampus using cranial magnetic resonance imaging based on deep learning convolutional neural networks, in particular, involving cranial magnetic resonance imaging based on deep learning convolutional neural networks A device and method for pairwise discrimination between Alzheimer's disease, amnestic mild cognitive impairment, and normal elderly groups using magnetic resonance imaging of the whole brain and / or hippocampus. Background technique [0002] Alzheimer's disease is a neurodegenerative disease. Existing research work has confirmed that in the MRI image files of the head, although there are individual differences in the MRI image data of each subject, in the sense of biostatistics, Alzheimer's disease, Alzheimer's disease, Amnestic mild cognitive impairment and the three catego...

Claims

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Application Information

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IPC IPC(8): G06T7/00G16H50/20
CPCG06T7/0012G16H50/20G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30016
Inventor 罗亚川
Owner 罗亚川
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