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Coronary artery CTA segmentation method and system based on multi-scale feature learning network

A multi-scale feature and deep learning network technology, applied in neural learning methods, biological neural network models, image analysis, etc., can solve problems such as inaccurate blood vessel segmentation, difficulty in effectively extracting multi-scale structural features, etc., and achieve fast and accurate segmentation Effect

Pending Publication Date: 2022-01-11
珠海横乐医疗科技有限公司
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AI Technical Summary

Problems solved by technology

Although the method based on deep learning has shown good results in the field of blood vessel segmentation, it is still difficult to effectively extract multi-scale structural features, resulting in inaccurate blood vessel segmentation

Method used

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  • Coronary artery CTA segmentation method and system based on multi-scale feature learning network
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  • Coronary artery CTA segmentation method and system based on multi-scale feature learning network

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Embodiment Construction

[0022] This part will describe the specific embodiment of the present invention in detail, and the preferred embodiment of the present invention is shown in the accompanying drawings. Each technical feature and overall technical solution of the invention, but it should not be understood as a limitation on the protection scope of the present invention.

[0023] In the description of the present invention, several means one or more, and multiple means more than two. Greater than, less than, exceeding, etc. are understood as not including the original number, and above, below, within, etc. are understood as including the original number.

[0024] In the description of the present invention, the continuous labeling of the method steps is for the convenience of review and understanding. In combination with the overall technical solution of the present invention and the logical relationship between each step, adjusting the implementation order between the steps will not affect the te...

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Abstract

The invention relates to a coronary artery CTA segmentation method and system based on a multi-scale feature learning network, and the method comprises the steps: collecting a coronary artery CTA image, extracting a sub-block image with a set size from the coronary artery CTA image, and carrying out the data expansion of the sub-block image, and obtaining a training set; training the training set through a deep learning network based on multi-scale feature extraction of a U-Net architecture to obtain a segmentation model for coronary artery CTA; performing segmentation processing on the acquired coronary artery CTA image through the segmentation model, and predicting to obtain a segmented coronary artery CTA image. The coronary artery CTA segmentation method has the beneficial effect that rapid and accurate coronary artery CTA segmentation is realized.

Description

technical field [0001] The invention relates to the fields of computer and medical treatment, in particular to a coronary CTA segmentation method and system based on a multi-scale feature learning network. Background technique [0002] Cardiovascular disease has become one of the largest causes of death in the world. Thanks to recent advances in multidetector CT technology, 3D CT angiography (CTA) has become the standard investigation for this disease. Extracting tubular arterial vascular structure is an important step in the detection and analysis of vascular abnormalities and lesions such as aneurysms, stenosis, and plaques. In addition, accurate and complete vascular structures can provide an important basis for hemodynamic analysis, functional assessment, and interventional surgery planning. [0003] With the ever-increasing clinical demands, manual extraction of coronary artery structures is a tedious and time-consuming process that is not feasible in clinical practic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30101G06T2207/20021G06N3/048G06N3/045
Inventor 朱建军王澄滕皋军
Owner 珠海横乐医疗科技有限公司
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