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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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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present invention provides a medical image processing method, device, image processing equipment and storage medium, which solves the problem of slow blood vessel segmentation speed in the prior art blood vessel segmentation method

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

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Embodiment 1

[0027] figure 1 It is a flow chart of the medical image processing method provided by Embodiment 1 of the present invention. The technical solution of this embodiment is applicable to the case of improving the processing speed of the medical image by reducing the amount of edge padding of the target image. The method can be executed by the medical image processing apparatus provided in the embodiment of the present invention, and the apparatus can be implemented in software and / or hardware, and configured to be applied in a processor of a medical image processing device. The method specifically includes the following steps:

[0028] S101. Divide the target image into a plurality of analysis image blocks through a first sliding window, or jointly divide the target image into a plurality of analysis image blocks of corresponding sizes through at least two second sliding windows with different window edge sizes, Wherein, the size of the window edge in each direction of the firs...

Embodiment 2

[0063] figure 2 It is a flow chart of the medical image processing method provided by Embodiment 2 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. Acquire a preset number of training image blocks from the preset image accuracy and number of training images.

[0065] Wherein, the preset image precision is preferably an image precision commonly used in clinical diagnostic images, and of course other image precisions, such as (1.0, 1.0, 1.0), may also be used. As long as the image precision of the training image blocks used to train the analysis model is the same as that of the analysis image blocks described in the foregoing embodiments.

[0066] The training images are clinical diagnostic images after image recognition processing. Taking a trained analysis model for analyzing CTA im...

Embodiment 3

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

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

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

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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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