Aortic dissection dynamic detection method based on morphology and deep learning

A morphology-based technology for aortic dissection, applied in image data processing, instrumentation, computing, etc., can solve the problems of not being widely promoted and low accuracy.

Inactive Publication Date: 2019-09-20
CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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[0007] However, the existing methods have n...

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  • Aortic dissection dynamic detection method based on morphology and deep learning
  • Aortic dissection dynamic detection method based on morphology and deep learning
  • Aortic dissection dynamic detection method based on morphology and deep learning

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Embodiment 1

[0050] Example 1: Basic principles of aortic dissection:

[0051] Aortic dissection refers to the fact that blood in the aortic cavity, driven by pulse pressure, directly penetrates the middle layer of the lesion through the intimal tear, resulting in separation of the middle layer; and extends along the direction of the artery to form a true and false lumen.

[0052] Figure 1-2 Shown is a CTA image of a patient with aortic dissection; the shape of the aorta changes as the scan location is changed. figure 1 , 2, superior vena cava refers to superior vena cava, trachea refers to trachea, Dssection of aortic arch dissection of aortic arch; Dissection of ascending aorta refers to ascending arterial dissection; left coronary artery refers to left coronary artery; pulmonary artery refers to pulmonary artery; dissection of descending aorta refers to descending Arterial dissection.

[0053] figure 1 A cross-sectional view of the aortic arch is shown, and the part circled by the y...

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Abstract

Aortic dissection (AD) is a dangerous cardiovascular disease, has extremely high clinical mortality, and has a sharp increase in the incidence rate in recent years. The invention discloses an aortic dissection dynamic detection method based on morphology and deep learning. The method comprises the steps of: 1, establishing a CTA sample set; and 2, carrying out deep learning based on CTA images in the CTA sample set, and carrying out disease detection; 2.1, performing deep learning based on the region of interest to obtain a learned convolutional neural network; and 2.2, for the new CTA image, performing disease detection based on the learned convolutional neural network in combination with the region of interest. The aortic dissection dynamic detection method based on the morphology and the deep learning is easy to implement and high in detection efficiency. Experiments show that the adopted deep learning method is far superior to a traditional method, and the method based on DenseNet 121 is more excellent.

Description

technical field [0001] The invention relates to a dynamic detection method for aortic dissection based on morphology and deep learning. Background technique [0002] Aortic dissection (AD) is a dangerous cardiovascular disease with extremely high clinical mortality rate, and its incidence has surged in recent years; the disease originates in the intima and media of the aortic wall Intimal tears; driven by pulse pressure, the blood in the aortic lumen directly penetrates the middle layer of the lesion through the intimal tear, resulting in separation of the middle layer; in recent years, the number of cases of this disease has increased day by day. [0003] Multi-slice CT angiography is a kind of non-diffusion angiography. By injecting contrast agent into the blood vessels to display blood vessels in different parts of the body, this technology can not only provide morphological information of lumen changes, but also display the vascular structure of blood vessels. wall lesi...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30101
Inventor 谭云谭凌向旭宇唐浩秦姣华
Owner CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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