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CT abdominal artery blood vessel grading recognition method based on deep learning

A deep learning and arterial blood vessel technology, applied in the field of blood vessel images, can solve the problems of intricate abdominal arteries, operational errors, time-consuming and low efficiency

Pending Publication Date: 2020-10-30
NANTONG UNIVERSITY +1
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

There are disadvantages in the traditional vascular grading method. The abdominal arteries are intricate. The separation of arteries and venous vessels and the grading of arteries need professionals to operate, which takes a long time and is inefficient. There will be certain operational errors. The method is to use the diameter information of blood vessels to realize the classification of blood vessels, but this method has defects. When the diameters of blood vessels are approximately equal, unnecessary blood vessels will be selected.

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  • CT abdominal artery blood vessel grading recognition method based on deep learning
  • CT abdominal artery blood vessel grading recognition method based on deep learning
  • CT abdominal artery blood vessel grading recognition method based on deep learning

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Embodiment Construction

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0078] see Figure 1 to Figure 10 , the present invention provides a technical solution: a deep learning-based CT abdominal artery classification recognition method, the method includes the following steps:

[0079] Step 1: Image preprocessing;

[0080] In terms of preprocessing, the following operations are performed on the blood vessel image:

[0081] Use the CLAHE algorithm to perform histogram equalization on the image;

[0082] CLAHE is an improvement ...

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Abstract

The invention discloses a CT abdominal artery blood vessel grading recognition method based on deep learning, and the method comprises the following steps: 1, carrying out the image preprocessing, andobtaining a training set; 2, performing image block clipping operation on the training set to obtain a data set; 3, performing blood vessel segmentation through deep learning; 4, performing skeletaltreatment; 5, searching an intersection point; 6, accumulating intersection points. The invention designs a blood vessel grading method, and grading of abdominal artery blood vessels is more accurately realized based on the thought of firstly segmenting and then grading.

Description

technical field [0001] The invention relates to the technical field of blood vessel images, in particular to a deep learning-based method for grading recognition of CT abdominal arteries. Background technique [0002] Abdominal arteries are one of the most important organs of human beings. Designing a systematic grading method can facilitate the study of the structure of abdominal arteries. The traditional vascular grading method has disadvantages. The arteries and vessels in the abdomen are intricate. The separation of arteries and venous vessels and the grading of arteries require professionals to operate, which takes a long time and is inefficient. There will be certain operational errors. The method is to use the diameter information of blood vessels to realize the classification of blood vessels, but this method has defects, when the diameters of blood vessels are approximately equal, unnecessary blood vessels will be selected. The present invention proposes a fully au...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/00G06T5/40G06N3/08G06N3/04
CPCG06T7/11G06T5/40G06T7/0012G06N3/08G06T2207/10081G06T2207/20084G06T2207/20081G06T2207/30101G06T2207/30028G06N3/045
Inventor 张堃韩宇范陆健范雷金冯文宇殷佳炜华亮李文俊鲍毅
Owner NANTONG UNIVERSITY
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