Medical image processing method and device, image processing equipment and storage medium

A medical image and processing method technology, applied in the field of medical image processing, can solve the problems of slow blood vessel segmentation and other problems

Pending Publication Date: 2020-10-30
SHANGHAI UNITED IMAGING HEALTHCARE
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The embodiment of the present invention provides a medical image processing method, device, image processing equipment and

Method used

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  • Medical image processing method and device, image processing equipment and storage medium
  • Medical image processing method and device, image processing equipment and storage medium
  • Medical image processing method and device, image processing equipment and storage medium

Examples

Experimental program
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Example Embodiment

[0026] Example one

[0027] figure 1 It is a flowchart of the medical image processing method provided in the first embodiment of the present invention. The technical solution of this embodiment is applicable to a situation where the processing speed of medical images is increased by reducing the number of edges of the target image. The method may be executed by the medical image processing apparatus provided by the embodiment of the present invention, and the apparatus may be implemented in software and / or hardware, and configured to be applied in the processor of the medical image processing equipment. The method specifically includes the following steps:

[0028] S101: Divide the target image into multiple analysis image blocks through the first sliding window, or jointly divide the target image into multiple analysis image blocks of corresponding sizes through at least two second sliding windows with different window edge sizes. Among them, the size of the window edges in eac...

Example Embodiment

[0062] Example two

[0063] figure 2 It is a flowchart of the medical image processing method provided in the second embodiment of the present invention. On the basis of the foregoing embodiments, the embodiment of the present invention adds an explanation of the analysis model training method. Such as figure 2 As shown, the training method includes:

[0064] S201: Obtain a preset number of training image blocks from training images with a preset image accuracy and number.

[0065] Among them, the preset image accuracy preferably adopts the image accuracy commonly used in clinical diagnosis images, of course, other image accuracy, such as (1.0, 1.0, 1.0), can also be used. As long as the image accuracy of the training image block used to train the analysis model is the same as the accuracy of the analysis image block described in the foregoing embodiment.

[0066] The training image is a clinical diagnosis image after image recognition processing. Taking the trained analysis mode...

Example Embodiment

[0075] Example three

[0076] image 3 It is a structural block diagram of the medical image processing device provided in the third embodiment of the present invention. The device is used to execute the medical image processing method provided in any of the foregoing embodiments, and the device can be implemented in software or hardware. The device includes:

[0077] The sliding window segmentation module 11 is used to divide the target image into multiple analysis image blocks through the first sliding window, or to divide the target image into corresponding size through at least two second sliding windows with different window edge sizes. A plurality of analysis image blocks, wherein the window edge sizes in each direction of the first sliding window and the second sliding window are determined based on the principle of the minimum number of edges;

[0078] The analysis module 12 is used to input the analysis image blocks into the trained analysis model in batches to obtain the ...

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Abstract

The embodiment of the invention discloses a medical image processing method and device, image processing equipment and a storage medium. The method comprises: segmenting the target image into a plurality of analysis image blocks through a first sliding window, or jointly segmenting the target image into a plurality of analysis image blocks with corresponding sizes through at least two second sliding windows with different window edge sizes, wherein the window edge sizes of the first sliding window and the second sliding windows in all directions are determined based on the principle that the number of complementary edges is minimum; inputting the analysis image blocks into a trained analysis model in batches to obtain a blood vessel distribution result of each analysis image block, whereinthe trained analysis model is formed by trained training image blocks of at least two sizes; and determining a blood vessel distribution result of the target image according to the blood vessel distribution result of each analysis image block. The problem that a blood vessel segmentation method in the prior art is low in blood vessel segmentation speed is solved.

Description

technical field [0001] Embodiments of the present invention relate to the field of medical image processing, and in particular, to a medical image processing method, device, image processing device, and storage medium. Background technique [0002] Head and neck vessel extraction is the most important and challenging task in angiography (CTA) technique. Head and neck arteries mainly include common carotid artery (CCA), internal carotid artery (ICA), external carotid artery (ECA), vertebral artery (VA), basilar artery (BA) and so on. The common carotid artery bifurcates into the internal carotid artery, which passes through the skull and supplies blood to the front and middle of the brain, and the external carotid artery, which supplies blood to the teeth and facial nerves. The left and right vertebral arteries travel through each vertebrae and finally merge into the basilar artery, which passes through the occipital bone and supplies blood to the back of the brain. In the ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06N3/08G06N3/04
CPCG06T7/0012G06T7/11G06N3/08G06T2207/20081G06T2207/20084G06T2207/30101G06T2207/10081G06N3/045
Inventor 毛玉妃李智
Owner SHANGHAI UNITED IMAGING HEALTHCARE
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