Unsupervised self-adaptive mammary gland lesion segmentation method

An adaptive model and image segmentation technology, applied in the field of image processing, can solve the problem of poor generalization ability of the segmentation model

Active Publication Date: 2020-05-19
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0008] In order to solve the shortcomings of the poor generalization ability of the segmentation model between domains mentio

Method used

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  • Unsupervised self-adaptive mammary gland lesion segmentation method
  • Unsupervised self-adaptive mammary gland lesion segmentation method
  • Unsupervised self-adaptive mammary gland lesion segmentation method

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Embodiment 1

[0086] Such as figure 1 As shown, the present invention proposes a method for adaptive image conversion between different fields. Based on this method, even if there is a difference between the new data set and the labeled data set, there is no need to label the images in the new data set, but through image conversion Adaptive learning is performed on two datasets. As a result, the unlabeled dataset retains its high-level semantic information through adaptive transformation, and its shallow representations such as image style, texture, and brightness are converted into the features of the labeled dataset, so that the trained dataset in the labeled dataset can be directly The network model is directly applied to the new dataset.

[0087] In the adaptive image conversion method according to one embodiment of the present invention, comprise the following steps:

[0088] First, a dataset containing annotations is required as the source domain image, which can be regarded as an a...

Embodiment 2

[0105] Such as figure 2 As shown, another embodiment of the present invention is an image segmentation method for unsupervised field adaptation, which can be specifically applied to computer-aided recognition of MRI images of breast lesions. It is mainly divided into: establishing an image domain discrimination network (S401), performing image reconstruction learning of target domain images (S402), optimizing based on the reconstruction network to obtain a conversion network (S403, S404), performing image processing on labeled source domain images. Segmentation network training (S405), converting the image of the target domain through the conversion network (S406), and then performing image segmentation on the converted target image by using the image segmentation network (S407).

[0106] In the above steps, the steps in S401, S402, S403, and S404 are the same as the corresponding steps in the first embodiment, and will not be repeated here.

[0107] Segmenting an annotated ...

Embodiment 3

[0116] see Figure 4 , the present embodiment provides a breast cancer screening device based on adaptive image segmentation, including:

[0117] An acquisition unit, configured to acquire source domain images in a source image set, where the images in the source domain image set contain marked feature regions, the source domain image set is a marked breast MRI image, and the feature regions are Lumps or areas of cancerous tissue to be marked;

[0118] It is also used to acquire a target domain image in the target domain image set, the target domain image is an unmarked breast MRI image, and the target domain image may contain an image part corresponding to a tumor or a cancerous tissue area;

[0119] An image domain discrimination unit, configured to take the source domain image and the target domain image acquired by the acquisition unit as input, and establish an image domain discrimination network Ncl for discriminating the domain to which the image belongs through a trai...

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Abstract

The invention provides a self-adaptive image segmentation method in the unsupervised field. The method includes: converting the target domain image to reserve semantic information of the target domainimage, and reconstructing shallow information of the image by taking the source domain image as a feature; and then performing image discrimination on the converted and reconstructed target domain image by utilizing a segmentation model established by the source domain image, thereby realizing migration between data sets of different domains of the model on the premise of no new data annotation.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an unsupervised-based adaptive breast lesion segmentation method. Background technique [0002] Breast cancer is the cancer with the highest incidence rate in women. Early diagnosis and treatment can effectively improve the long-term survival rate of breast cancer patients. As a multi-parameter, multi-contrast imaging technique, Magnetic Resonance Imaging (MRI) can reflect tissue T1, T2 and proton density and other characteristics, and has the advantages of high resolution and sensitivity. One of the important tools for screening. Breast MRI technology has been increasingly used in clinical practice, especially in the early screening of breast cancer. [0003] In breast cancer MRI screening, computer-assisted image analysis is a development trend and a core technical issue in this field. Early medical image segmentation initially used edge detection, texture features, morpholog...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T3/00
CPCG06T7/11G06T3/0012G06T2207/10088G06T2207/20081G06T2207/20084G06T2207/30068G06T2207/30096Y02T10/40
Inventor 李程王珊珊肖韬辉郑海荣刘新梁栋
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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