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A CT image processing method for predicting the prognosis of new coronary pneumonia

A CT image and image processing technology, applied in the medical field, can solve problems such as model overfitting

Active Publication Date: 2021-02-02
INST OF ENVIRONMENTAL MEDICINE & OCCUPATIONAL MEDICINE ACAD OF MILITARY MEDICINE ACAD OF MILITARY SCI
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in practice, it is found that if only simple splicing and fusion of different features is performed, many redundant features may be included, resulting in model overfitting

Method used

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  • A CT image processing method for predicting the prognosis of new coronary pneumonia
  • A CT image processing method for predicting the prognosis of new coronary pneumonia

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with the accompanying drawings. It should be noted that this embodiment is based on the technical solution, and provides detailed implementation and specific operation process, but the protection scope of the present invention is not limited to the present invention. Example.

[0036] The present invention comprises the following steps:

[0037] S1. Automatic lung segmentation based on K-means

[0038] In order to extract the lung features in CT images, the lung regions are first automatically segmented from the CT images. In CT images, the CT values ​​of the lung area and human muscle, bone and other tissues are quite different, and the lung area can be segmented by thresholding. However, due to differences in scanning machines, the optimal lung segmentation threshold in each patient's CT image is different. In addition, if a larger segmentation threshold is used, it will lead to over-segmentation, ...

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Abstract

The invention discloses a CT image image processing method based on deep learning-radiomics fusion features for predicting the prognosis of new coronary pneumonia, comprising the following steps: S1, automatic lung segmentation algorithm based on K-means; S2, lung image Omics feature extraction; S3, deep learning feature extraction; S4, deep learning and radiomics differential feature learning; S5, image processing of poor prognosis prediction model based on fusion features. The method of the present invention retains the advantages of the two methods, the interpretability of radiomics features and the data adaptability of deep learning features. At the same time, the present invention proposes a differential feature learning method, which makes deep learning features and radiomics features complementary, reduces the redundancy of the two features, and can further improve the accuracy of prognosis prediction.

Description

technical field [0001] The present invention relates to a medical technology, in particular to a CT image processing method based on deep learning-radiomics fusion features for predicting the prognosis of new coronary pneumonia. Background technique [0002] The prognosis of different patients with new coronary pneumonia varies greatly. After timely treatment, most patients with new coronary pneumonia have a good prognosis, but about 7% of patients with new coronary pneumonia will have a poor prognosis after diagnosis, that is, they need mechanical ventilation or develop die. For this part of patients with new coronary pneumonia with poor prognosis, if they can be predicted in the early stage of diagnosis, intervention or new treatment plans can be planned in advance to reduce the probability of poor prognosis. Therefore, predicting patients with poor prognosis at the early stage of diagnosis of new coronary pneumonia is of great significance for the treatment plan planning...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/136G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T7/136G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30061G06N3/045G06F18/22G06F18/23213G06F18/253
Inventor 薛新颖高全胜薛志强王志军
Owner INST OF ENVIRONMENTAL MEDICINE & OCCUPATIONAL MEDICINE ACAD OF MILITARY MEDICINE ACAD OF MILITARY SCI
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