Intravascular stent image segmentation method and system based on double attention mechanism

An image segmentation and attention technology, applied in the field of image recognition, can solve the problem of inability to segment intravascular stents in real time

An image segmentation and attention technology, applied in the field of image recognition, can solve the problem of inability to segment intravascular stents in real time

CN111986181AActive Publication Date: 2020-11-24INST OF AUTOMATION CHINESE ACAD OF SCI

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  • Intravascular stent image segmentation method and system based on double attention mechanism
  • Intravascular stent image segmentation method and system based on double attention mechanism
  • Intravascular stent image segmentation method and system based on double attention mechanism

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[0076] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0077] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0078] The present invention provides a kind of intravascular stent image segmentation method based on double attention mechanism, and this method comprises:

[0079] Step S10, acquiring the X-ray transmission video sequence of the area containing the stent during the operation as t...

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Abstract

The invention belongs to the field of intravascular stent image segmentation, particularly relates to an intravascular stent image segmentation method and system based on a double attention mechanism,and aims to solve the problem that an intravascular stent cannot be accurately segmented from an intraoperative X-ray transmission image in real time in the prior art. The present invention comprises: an X-ray transmission to-be-detected video sequence is acquired, and a segmentation mask sequence for displaying an intravascular stent is generated through a lightweight upper attention fusion network based on deep learning based on the to-be-detected video sequence, and the to-be-detected video sequence is covered with the binary segmentation mask for displaying the intravascular stent to generate a video sequence for displaying the intravascular stent. According to the invention, the accuracy of intravascular stent image segmentation is improved by adopting the feature attention blocks and the associated attention blocks, model training is carried out by adopting the Dice loss function and the focusing loss function, wrong classification of edge pixels is avoided, and the performanceof an image classification network is improved.

Description

technical field [0001] The invention belongs to the field of image recognition, and in particular relates to a method and system for segmenting images of intravascular stents based on a double-attention mechanism. Background technique [0002] Abdominal aortic aneurysm (AAA) is the most common type of aneurysm. Abdominal aortic aneurysms usually do not present with typical symptoms until they rupture, and thus typically result in an 85% to 90% case-fatality rate. Clinical studies have shown that compared with open repair methods, endovascular aneurysm repair (EVAR) surgery can effectively reduce the perioperative morbidity and mortality of patients, and maintain the same degree of postoperative survival. However, due to the complexity of EVAR surgery, prolonged radiation and large doses of contrast medium injections are usually required during interventional procedures, which may lead to common complications such as renal failure in patients. Therefore, it is very necessar...

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Application Information

Patent Timeline
24 Nov 2020
Publication
CN111986181A
IPC
G06T7/00; G06T7/11; G06N3/08; G06N3/04
CPC
G06T7/0012; G06T7/11; G06N3/08; G06T2207/10081; G06T2207/10116; G06T2207/20081; G06T2207/20084; G06T2207/30101
Inventors
刘市祺; 谢晓亮