Medical image pulmonary nodule detection method based on a cyclic feature pyramid

A technology of cyclic features and medical images, applied in the field of computer vision, can solve the problems of increasing the complexity of the classifier, easily missing small nodules, and low detection accuracy, so as to improve the generalization ability, simplify the detection process, and improve the detection accuracy. The effect of precision

Active Publication Date: 2019-06-21
XIDIAN UNIV
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Problems solved by technology

The common defect of these methods is that it needs to rely on the user's prior knowledge and a lot of attempts to determine the rules and select parameters, which increases

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  • Medical image pulmonary nodule detection method based on a cyclic feature pyramid
  • Medical image pulmonary nodule detection method based on a cyclic feature pyramid
  • Medical image pulmonary nodule detection method based on a cyclic feature pyramid

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Embodiment Construction

[0033] The embodiments and effects of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0034] refer to figure 1 , the concrete steps of the present invention are as follows:

[0035] Step 1, acquire medical images.

[0036] Select 1000 chest scan images from the public data set LIDC-IDRI, extract the coordinate position information of lung nodules by reading the XML format annotation file of the original database, and use the desensitized chest scan image and lung node coordinate information To form a sample data set, 700 chest scan images in the sample data set are used as a training data set, and 300 chest scan images are used as a data test set.

[0037] Step 2, expand the sample data set.

[0038] refer to figure 2 , this step is specifically implemented as follows:

[0039] (2a) Introduce uniform noise J as the input of the generation network G in the DCGAN network to generate a picture data set F;

[...

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Abstract

The invention discloses a medical image pulmonary nodule detection method based on a cyclic feature pyramid, and mainly solves the problem of low detection precision caused by high extraction difficulty of pulmonary nodules and ground glass nodules in the prior art. The method comprises the following implementation steps: 1) acquiring a medical image; 2) preprocessing the medical image, and expanding a sample data set; 3) constructing a circular feature pyramid detection model by combining the feature map; 4) training the detection model by using the extended data set sample to obtain a targetdetection model; and 5) inputting the test set in the data set into the trained detection model to carry out pulmonary nodule detection. The method constructs a new feature extraction network, accelerates the network training speed, enhances the sensitivity to nodules of different sizes, improves the detection precision of pulmonary nodules of medical images, and can be used for a computer-aidedmedical diagnosis system.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a method for detecting pulmonary nodules in medical images, which can be used to identify medical images. Background technique [0002] According to the National Cancer Center, the incidence of lung cancer ranks second among cancers, and its early symptoms are mostly in the form of pulmonary nodules. Therefore, the detection and diagnosis of lung nodules is of great significance to save lung cancer patients. In recent years, with the deterioration of people's living environment and changes in living habits, the number of people with lung cancer has shown an increasing trend. At the same time, the high mortality rate of lung cancer has made the society pay more and more attention to it. The number of CT loaders in major hospitals in my country has increased significantly. Combined with the high-resolution CT images of the lungs they provide, the detection method based on the feature...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
Inventor 姬红兵朱文文张文博刘思成段育松
Owner XIDIAN UNIV
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