A hepatocellular carcinoma automatic grading method based on an SE-DenseNet deep learning framework and a multi-modal enhanced MR image

A technology for automatic grading of hepatocellular carcinoma, applied in the field of medical image processing, which can solve the problems of subjectivity differences, manpower and time consumption

Active Publication Date: 2019-06-14
LISHUI CENT HOSPITAL +1
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problems of manpower, time consumption and subjectivity differences in the traditional manual ...

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  • A hepatocellular carcinoma automatic grading method based on an SE-DenseNet deep learning framework and a multi-modal enhanced MR image
  • A hepatocellular carcinoma automatic grading method based on an SE-DenseNet deep learning framework and a multi-modal enhanced MR image
  • A hepatocellular carcinoma automatic grading method based on an SE-DenseNet deep learning framework and a multi-modal enhanced MR image

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[0075] The present invention will be further described in detail below with reference to the embodiments, so that those skilled in the art can implement it according to the text of the description.

[0076] It should be understood that terms such as "having", "including" and "including" used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0077] The method for automatic classification of hepatocellular carcinoma based on the SE-DenseNet deep learning framework and enhanced MR images of this embodiment includes the following steps:

[0078] 1) Obtain preoperative three-dimensional images and pathological grading results of multimodal enhanced MR of hepatocellular carcinoma;

[0079] 2) Preprocessing all the 3D images of hepatocellular carcinoma with enhanced MR as training data;

[0080] 3) Enhance the training data to increase the amount of training data;

[0081] 4) Based on the enhanced training data, train the hepatocellular carc...

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Abstract

The invention discloses a hepatocellular carcinoma automatic grading method based on an SE-DenseNet deep learning framework and an enhanced MR image. The method comprises the following steps of 1) collecting data; 2) preprocessing all hepatocellular carcinoma three-dimensional images with enhanced MR; 3) enhancing the training data; 4) based on the enhanced training data, training a hepatocellularcarcinoma grading prediction model, namely an SE-DenseNet network; and 5) carrying out hierarchical prediction on the test data by adopting the trained model, and evaluating the classification performance of the hepatocellular carcinoma hierarchical prediction model. According to the automatic pathological grading method for the hepatocellular carcinoma multi-modal enhanced MR image which is composed of the steps of image preprocessing, the image enhancement, the hepatocellular carcinoma multi-modal enhanced MR image classification, SE-DenseNet network training and SE-DenseNet network testing, the hepatocellular carcinoma automatic grading can be realized, and the problems of manpower consumption, time consumption and subjective difference existing in manual hepatocellular carcinoma grading can be solved.

Description

Technical field [0001] The present invention relates to the field of medical image processing, in particular to an automatic classification method of hepatocellular carcinoma based on SE-DenseNet deep learning framework and multimodal enhanced MR images. Background technique [0002] Globally, lung / liver / stomach and bowel tumors currently account for nearly half (46%) of all cancer deaths. Hepatocellular carcinoma (HCC), fibrous membrane carcinoma, cholangiocarcinoma, angiosarcoma and hepatoblastoma are called primary liver cancers. Among these primary liver cancers, hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide Large tumors, more than 500,000 people are affected by this disease. In the grades of hepatocellular carcinoma, grades II and III account for the majority. The prognosis of liver cancer patients is related to the differentiation degree of liver cancer and the treatment plan of liver cancer. Therefore, the classification of liver ca...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04
Inventor 纪建松戴亚康周志勇徐民陈敏江周庆
Owner LISHUI CENT HOSPITAL
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