A hippocampus segmentation method based on sequence learning

A hippocampal and sequence technology, applied in the fields of computer vision and deep learning, can solve the problems of low segmentation accuracy and long segmentation time, and achieve the effect of speeding up training speed, reducing parameters, and shortening training time.

Active Publication Date: 2019-04-05
无锡本希奥智能技术有限公司
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Problems solved by technology

[0005] The present invention provides a method for segmenting the hippocampus based on sequence learning, aiming to s...

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  • A hippocampus segmentation method based on sequence learning
  • A hippocampus segmentation method based on sequence learning
  • A hippocampus segmentation method based on sequence learning

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Embodiment Construction

[0053] The present invention will be further described below in combination with specific embodiments.

[0054] In order to verify the effectiveness of the method, an experiment was carried out on the ADNI database. The experimental data of the present invention consisted of 120 groups of brain MRI images, and the 120 groups included real patients and healthy comparison groups. In order to verify the performance of the model, the data is divided into 10 parts, and a 10-fold cross-validation experiment is used, 9 parts are used for training, and 1 part is used for testing until all the data are tested. Regarding the optimization algorithm of the model, the Nadam algorithm is used, the learning rate is set to 0.001, and the weight initialization uses the glorot uniform distribution initialization method.

[0055] The hardware equipment is as follows: processor Intel Core i7-9700K CPU@4.2GHz; memory (RAM) 32.0GB; discrete graphics card, NVIDIA GeForce GTX 1070; system type, Ubunt...

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Abstract

The invention relates to the field of computer vision and deep learning, in particular to a hippocampus segmentation method based on sequence learning. The method comprises the following steps: step 1, preprocessing an original image set A; 2, building a network model, wherein the hippocampus segmentation network model comprises an encoding part, a bidirectional convolution long and short memory network and a decoding part; Step 3, a model is trained; And performing forward propagation on the anatomical plane image set D, E and F to obtain a single iteration result, and calculating a loss function to obtain weight models J, K and L through backward propagation. According to the method, the hippocampus structure in the human brain nuclear magnetic resonance image is efficiently, automatically and accurately segmented by utilizing the method based on the deep learning network, and the operation speed is relatively high while the high segmentation precision is ensured. Besides detection of hippocampus, the network provided by the invention can be retrained, so that the network can be applied to detection and segmentation of other organs or tissues.

Description

technical field [0001] The invention relates to the fields of computer vision and deep learning, in particular to a method for segmenting hippocampus based on sequence learning. Background technique [0002] The hippocampus is an important part of the brain's nervous system. The abnormal volume and function of the hippocampus are closely related to many mental diseases, such as: temporal lobe epilepsy (Temporal Lobe Epilepsy, TLE), Alzheimer's disease (Alzheimer's Disease, AD ), Schizophrenia, etc. Therefore, accurate segmentation of the hippocampus can assist doctors in the diagnosis and treatment of related mental diseases, which has great medical value. MRI images can provide three-dimensional brain tissue information with rich contrast and high resolution, and are important data for studying the morphology of the hippocampus. Therefore, studying the volumetric shape of the hippocampus in brain MRI images and realizing accurate segmentation of the three-dimensional hipp...

Claims

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Application Information

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IPC IPC(8): G06T7/10
CPCG06T7/10G06T2207/20132G06T2207/20084G06T2207/20081
Inventor 肖志勇刘辰
Owner 无锡本希奥智能技术有限公司
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