Vascular model extraction method based on depth neural network

A technology of deep neural network and blood vessel model, which is applied in biological neural network model, neural learning method, image data processing, etc., can solve the problems of low efficiency of 3D extraction operation, difficult to consider the 3D characteristics of blood vessels, etc., and achieve high accuracy , a wide range of applications, the effect of high extraction accuracy

Active Publication Date: 2019-03-15
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

[0005] It is difficult to take into account the three-dimensional characteristics of blood vessels in general two-dimensional extraction, and the operation efficiency of three-dimensional extraction is not high, and there are different degrees of isolated points or non-vascular areas in the extraction results

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  • Vascular model extraction method based on depth neural network
  • Vascular model extraction method based on depth neural network
  • Vascular model extraction method based on depth neural network

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[0026] In order to be able to understand the above objectives, features and advantages of the present invention more clearly, the present invention will be further described in detail below with respect to specific embodiments.

[0027] To achieve the above objective, the present invention adopts the following technical solutions:

[0028] A method for extracting a blood vessel model based on a deep neural network includes the following steps:

[0029] Step 1: Enhance blood vessel data. Through the T_Frangi algorithm, according to the spatial scale theory, when the scale factor matches the actual width of the blood vessel, the response value of the blood vessel structure is the largest, thereby enhancing the blood vessel data;

[0030] Step 2: Candidate data retention, calculate the ratio of the candidate data retained under different response values ​​to the original blood vessel data, and select a response value greater than and close to the volume ratio to retain the candidate data ...

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Abstract

The invention discloses a blood vessel model extraction method based on a depth neural network. The method comprises the following steps: 1. Blood vessel data is enhanced by T_Frangi algorithm according to the spatial scale theory so as to enhance the blood vessel data; 2, reserve candidate data; 3, calculate that characteristics of the blood vessel connection region; Step 4: Deep neural network training, using the tuple of blood vessel features as input to train the neural network, so as to obtain the blood vessel extraction model. The blood vessel model extraction method based on the depth neural network, In the candidate region of blood vessel after depth enhancement, Using the connectivity of blood vessels, the five-element feature set of each connected region is constructed, and the neural network model is trained to extract blood vessels. This method does not need to train the whole volume data, only needs to consider the calculation of candidate blood vessel connectivity regions. It can effectively remove the outliers, and has high extraction accuracy and flexibility.

Description

Technical field [0001] The invention relates to the technical field of medical image processing, in particular to a method for extracting a blood vessel model based on a deep neural network. Background technique [0002] Vascular extraction is the cornerstone of many medical imaging applications, such as acute ischemic stroke. Vascular extraction is very important for the quantification of vascular occlusion and the evaluation of collateral flow. In the diagnosis of coronary atherosclerosis, vascular extraction is the detection of luminal stenosis. An essential step. [0003] Vessel extraction algorithms can generally be divided into two categories: one is active contour models, such as geodesic active contour models. Because the gray value at the edge of the blood vessel is similar to the gray value of the surrounding tissue, it is difficult to accurately segment the blood vessel by using the boundary gradient and regional information; and the active contour model needs to be adj...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/187G06T7/11G06T7/00G06N3/08
CPCG06N3/084G06T7/0012G06T2207/30101G06T7/11G06T7/187
Inventor 赵世凤田沄王学松周明全
Owner BEIJING NORMAL UNIVERSITY
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