Medical image segmentation method based on DR-Unet104
A dr-unet104, medical image technology, applied in the field of medical image segmentation based on DR-Unet104, can solve the problem of low feature utilization, achieve the effect of improving generalization ability, segmentation performance and overall performance
Pending Publication Date: 2021-04-27
山西三友和智慧信息技术股份有限公司
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[0004] Aiming at the technical problem of low feature utilization in the segmentation method based on deep learning, the present invention provides a tunnel crack detection and measurement method based on dual deep learning models with strong segmentation performance, high accuracy and high efficiency
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[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0023] A medical image segmentation method based on DR-Unet104, such as figure 1 shown, including the following steps:
[0024] S100. Data collection: constructing an original data set by collecting relevant medical images;
[0025] S200. Data expansion: performing data enhancement on the original data set to realize data set expansion;
[0026] S300. Data processing: including division, standardization and unification of data scales of data sets;
[0027] ...
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The invention belongs to the technical field of image segmentation, and particularly relates to a medical image segmentation method based on DR-Unet104. The method comprises the following steps of: data acquisition: constructing an original data set by acquiring related medical images; data expansion: performing data enhancement on the original data set to realize data set expansion; data processing, including division and standardization of data sets and unification of data scale sizes; and model construction: carrying out model training by using a DR-Unet104 model. According to the method, the generalization ability of the model is improved through preprocessing methods such as standardization and data expansion, the segmentation performance of the model is improved by combining the advantages of a basic Unet model and a residual connection module, the overall performance of the model is further improved by using dropout, and a great auxiliary effect is provided for medical diagnosis of doctors. The method is used for medical image segmentation.
Description
technical field [0001] The invention belongs to the technical field of image segmentation, and in particular relates to a medical image segmentation method based on DR-Unet104. Background technique [0002] Segmentation of lesions is an important research area necessary to advance the field of radiology, using imaging to infer biomarkers to help predict and treat the prognosis of patients Large, and the intensity and contrast are inconsistent, resulting in the segmentation of medical images requiring skilled and professional medical personnel to perform manual segmentation, which is time-consuming. [0003] Reasons for problems or defects: At present, segmentation methods based on deep learning are widely used in medical image segmentation tasks. However, due to the blurred edges and small target areas of related medical images, some segmentation methods based on deep learning have characteristics. Problems such as low utilization rate eventually lead to difficulty in impro...
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IPC IPC(8): G06T7/10
CPCG06T7/10G06T2207/20081G06T2207/20084G06T2207/30096Y02T10/40
Inventor 潘晓光张海轩张娜刘剑超
Owner 山西三友和智慧信息技术股份有限公司