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Medical image segmentation method and device, storage medium and electronic equipment

A medical image and image segmentation technology, applied in image analysis, neural learning methods, image data processing, etc., can solve the problem of uneven size of lesions, and achieve the effect of avoiding the disappearance of lesions

Pending Publication Date: 2022-04-15
HYGEA MEDICAL TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the problem of image segmentation caused by the uneven size of lesions in organs and organs, the present invention provides a medical image segmentation method, device, storage medium and electronic equipment

Method used

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  • Medical image segmentation method and device, storage medium and electronic equipment
  • Medical image segmentation method and device, storage medium and electronic equipment
  • Medical image segmentation method and device, storage medium and electronic equipment

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Experimental program
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Embodiment 1

[0048] figure 1 A flow chart of a medical image segmentation method is shown, as figure 1 As shown, this embodiment provides a medical image segmentation method, including step S110 to step S120:

[0049] Step S110, acquiring the medical image to be segmented;

[0050] Step S120, using the trained image segmentation model to segment the medical image to be segmented to obtain the first segmentation result of the target organ and the background and the second segmentation result of the target organ lesion and the background respectively, and combine the first segmentation result and the second segmentation result The two segmentation results are superimposed and feature fusion is performed to obtain the third segmentation result of the background, target organ and target organ lesion;

[0051] Wherein, the image segmentation model includes: a first neural network model whose input is the medical image to be segmented and whose output is the first segmentation result, and a se...

Embodiment 2

[0110] Corresponding to Embodiment 1, this embodiment provides a medical image segmentation device, such as Figure 8 shown, including:

[0111] An acquisition module 810, configured to acquire a medical image to be segmented;

[0112] The segmentation module 820 is used to segment the medical image to be segmented by using the trained image segmentation model, respectively obtain the first segmentation result of the target organ and the background and the second segmentation result of the target organ lesion and the background, and divide the first segmentation The result and the second segmentation result are superimposed and feature fusion is performed to obtain the third segmentation result of the background, target organ and target organ lesion;

[0113] Wherein, the image segmentation model includes a first neural network model and a second neural network model, the input of the first neural network model includes the medical image to be segmented, and the output includ...

Embodiment 3

[0137] This embodiment provides a storage medium, on which a computer program is stored, and when the computer program is executed by one or more processors, the medical image segmentation method according to the first embodiment is realized.

[0138] In this embodiment, the storage medium can be realized by any type of volatile or non-volatile storage device or their combination, such as Static Random Access Memory (Static Random Access Memory, SRAM for short), Electrically Erasable and Programmable Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short) , read-only memory (Read-Only Memory, ROM for short), magnetic memory, flash memory, magnetic disk or optical disk. For the details of the method, see Embodiment 1, which will not be repeated this time.

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Abstract

The invention provides a medical image segmentation method and device, a storage medium and electronic equipment. The medical image segmentation method comprises the following steps: acquiring a to-be-segmented medical image; segmenting the medical image to be segmented by using a trained image segmentation model to obtain a first segmentation result of a target visceral organ and a background and a second segmentation result of a target visceral organ lesion and the background respectively, and superposing the first segmentation result and the second segmentation result and performing feature fusion to obtain a target visceral organ lesion image; and obtaining a third segmentation result of the background, the target visceral organ and the target visceral organ focus. According to the method, two different neural network structures are fused in one neural network framework to segment the visceral organs and the lesions respectively, and the segmentation results of the two neural network structures are fused to obtain a final overall segmentation result, so that the image segmentation problem caused by non-uniform sizes of the visceral organs and the lesions in the visceral organs is solved.

Description

technical field [0001] The present invention relates to the technical field of image segmentation, in particular to a medical image segmentation method, device, storage medium and electronic equipment. Background technique [0002] In recent years, the rapid development of minimally invasive surgery has brought hope to patients with poor tolerance. With the advancement of artificial intelligence technology, in the field of multi-modal cold and hot ablation, the user's demand for the automatic planning system of ablation needle is also increasing. Since the automatic planning of the ablation needle depends on the 3D reconstruction of human organs, and the basis of the 3D reconstruction is the accurate positioning of the liver and its lesion area in each CT image, in order to achieve accurate and rapid automatic planning of the ablation needle, it is necessary to The liver and lesion area of ​​each CT image are accurately segmented. [0003] In 2015, Ronneberger et al. propo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/194G06N3/04G06N3/08
Inventor 张雨萌黄乾富
Owner HYGEA MEDICAL TECH CO LTD