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Carotid plaque recognition method and system for cross-modal prediction from CTA to MRA

An identification method and identification system technology, applied in the carotid plaque identification algorithm and carotid plaque identification field, can solve problems such as trauma, and achieve the effect of improving the accuracy rate, improving the plaque image quality, and improving the identification accuracy rate.

Active Publication Date: 2020-09-15
复影(上海)医疗科技有限公司
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Problems solved by technology

The disadvantage is that arterial cannulation is required, there is trauma, and hospitalization is required

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  • Carotid plaque recognition method and system for cross-modal prediction from CTA to MRA
  • Carotid plaque recognition method and system for cross-modal prediction from CTA to MRA
  • Carotid plaque recognition method and system for cross-modal prediction from CTA to MRA

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Embodiment Construction

[0073] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0074] A carotid plaque identification method for cross-modal prediction from CTA to MRA provided by the present invention comprises the following steps:

[0075] Data collection step: collect paired carotid artery CTA and MRA image data;

[0076] Plaque segmentation and model training steps: firstly perform CTA and MRA carotid lumen positioning, then carotid plaque segmentation, obtain plaque area images of CTA and MRA, and send the plaque area images of CTA and MRA to The pix2pix or cycle-GAN network...

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Abstract

The invention provides a carotid plaque identification method based on cross-modal prediction from CTA to MRA. The method comprises the following steps: a data collection step: paired carotid CTA andMRA image data are collected; plaque segmentation and model training: carotid artery lumens of CTA and MRA are positioned firstly, then carotid artery plaques are segmented, plaque area images of CTAand MRA are obtained, the plaque area images of CTA and MRA are sent to a pix2pix or cycle-GAN network to be trained, and a model obtained after preliminary training is obtained. According to the method, a complete and novel algorithm flow is designed, the carotid artery lumen and plaque on the CTA image are automatically segmented by using an improved deep learning Multiplan-net algorithm, and anoptimized adversarial generative network is designed on the basis to perform CTA data amplification, so that the segmentation precision is further improved.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a carotid plaque recognition method and system for cross-modal prediction from CTA to MRA. In particular, it relates to a carotid plaque recognition algorithm for cross-modal prediction of CTA images into the domain of MRA images. Background technique [0002] Cerebrovascular disease is recognized worldwide as one of the diseases with the highest morbidity and mortality, including hemorrhagic cerebrovascular disease and ischemic cerebrovascular disease. Among them, ischemic cerebrovascular disease accounted for 87% [1] . Since the carotid artery is one of the main arteries supplying blood to the brain, severe atherosclerosis in the carotid artery may lead to cerebral ischemia. The obvious feature of carotid atherosclerosis is the appearance of carotid plaque in the arterial lumen [2] , Plaque composition is different, its vulnerability and stability are also di...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/33G06T7/73G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T7/33G06T7/73G06N3/08G06T2207/10081G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30101G06N3/045G06F18/241Y02A90/10
Inventor 耿道颖于泽宽张军尹波李郁欣吴昊耿岩胡斌杨丽琴张晓龙狄若愚
Owner 复影(上海)医疗科技有限公司
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