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

A medical image and processing method technology, applied in the field of image processing, can solve the problems of low contrast, difficult to accurately identify the boundary of the lesion area, low segmentation accuracy of the ischemic lesion area, etc. , the effect of improving the accuracy

Active Publication Date: 2021-10-15
BEIJING TIANTAN HOSPITAL AFFILIATED TO CAPITAL MEDICAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The spatial geometry and location of the ischemic lesion in each slice of the brain CT image are variable, the contrast between the lesion area and the normal area is low, and the boundary of the lesion area is difficult to identify accurately, which makes the brain CT image The accuracy of ischemic lesion segmentation is low

Method used

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

Examples

Experimental program
Comparison scheme
Effect test

experiment example 1

[0143] Experimental Example 1 Performance Evaluation and Comparative Experiment

[0144] We have evaluated the technical scheme in the embodiment of the present invention (such as Figure 5 shown) and the performance of the comparison models, where the comparison models include U-Net, U-Net++, PSP-Net, DeepLabv3+, SF-Net, Inf-Net, CE-Net, OC-Net and the newly proposed Swin-UperNet ( transformer model). In addition, unless otherwise specified, the technical solutions in the embodiments of the present invention use ResNet-16 as the backbone network for feature extraction. The segmentation performance results of the inventive and comparative models are shown in Table 1.

[0145]As shown in Table 1, the technical solution in the embodiment of the present invention has reached the highest value in terms of Dice index, IoU and sensitivity, which are 1.3%, 1.2% and 3.1% higher than the second-ranked model respectively. In terms of specificity, there was little difference between a...

Embodiment 2

[0149] Embodiment 2 Backbone network replacement comparison experiment

[0150] This experimental example is in Figure 5 On the basis of the technical solution, the feature extraction module was replaced for experiments, and then the replaced technical solution was compared with other models with the same backbone network. The results are shown in Table 2. Among them, IS-Net basically adopts Figure 5 In the technical scheme, only different backbone networks are used as its feature extraction modules.

[0151] Table 2

[0152]

[0153] As shown in Table 2: For each backbone network, the IS-Net of the present invention is compared with a benchmark test mode, that is, IS-Net with backbone network ResNet-16 compared with DeepLabv3+, IS-Net with backbone network Res2Net Net and Inf-Net comparison, IS-Net with backbone network Swin-T and Swin-UperNet comparison. Figures in parentheses illustrate the improvement of IS-Net over comparable models with the same backbone network...

experiment example 3

[0154] Experimental example 3 fusion strategy replacement comparison experiment

[0155] This experimental example is in Figure 5 Based on the technical solution, different fusion strategies are used to replace the feature pyramid module. The schemes with different fusion strategies were used to segment the lesion area, and the segmentation performance was evaluated and compared. The results are shown in Table 3.

[0156] table 3

[0157] fusion strategy Dice (%) IoU(%) Sens.(%) edge constraints 67.5 56.7 74.4 marginal attention 66.5 55.3 71.8 FAM 67.3 56.1 74.3 FPN 67.0 55.8 73.6

[0158] Different integration strategies such as Figure 10 shown. Among them, FPN enhances the low-level high-level feature map by additively fusing the up-sampled high-level high-level feature map. FAM is a feature alignment module that enhances information dissemination between high-level high-level feature maps and low-level high-level fe...

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Abstract

The invention discloses a medical image processing method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-processed medical image; performing hierarchical feature extraction on the medical image by using a multi-level feature extraction network to obtain a multi-level feature map, the multi-level feature map including a plurality of low-level feature maps and a plurality of high-level feature maps, the edge information included in the low-level feature maps being more than the edge information included in the high-level feature maps; obtaining a lesion region boundary diagram by using the low-level feature map; obtaining a spliced feature map based on the plurality of advanced feature maps and the lesion region boundary map; and carrying out image segmentation processing based on the spliced feature map to obtain a lesion region segmentation map. According to the technical scheme provided by the embodiment of the invention, the accuracy of lesion region segmentation of the medical image can be improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, and more specifically, to a medical image processing method, device, electronic equipment and storage medium. Background technique [0002] Generally speaking, the objects of medical image processing are medical images obtained by various imaging mechanisms. Common clinical medical imaging mainly includes X-ray imaging, angiography, cardiovascular angiography, computed tomography imaging, mammography, and positron emission. Tomography, MRI, nuclear medicine imaging, and ultrasound imaging, etc. [0003] In recent years, the use of computer image processing technology to process medical images has become a research and development hotspot. For example, based on computer image processing technology, two-dimensional slice images are analyzed and processed to realize segmentation, extraction and three-dimensional reconstruction of human organs, soft tissues and diseased bodies. and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/12G06T5/50G06T3/40G06T5/30G06K9/46G06N3/04G06N3/08
CPCG06T7/12G06T3/4038G06T5/50G06T5/30G06N3/08G06T2207/10081G06T2207/20016G06T2207/20221G06T2207/30016G06N3/045
Inventor 聂曦明王龙刘丽萍
Owner BEIJING TIANTAN HOSPITAL AFFILIATED TO CAPITAL MEDICAL UNIV
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