Liver cancer image classification method combining computer vision features and radiomics features
A technology of computer vision and radiomics, applied in the field of medical image processing, to achieve superior performance
Active Publication Date: 2021-11-05
ZHEJIANG UNIV
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However, radiomics features are mostly manual features, which are calculated by shallow mathematical formulas and are easily affected by noise and low-level image features.
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[0111] The method of the present invention will be further described below in conjunction with the accompanying drawings.
[0112] Step (1). Acquisition of patient liver cancer image data and image preprocessing.
[0113] Inclusion criteria of the data: Hepatocellular carcinoma was clearly diagnosed pathologically, and the pathological grade had been clearly defined; liver CT plain scan plus enhanced examination was performed within 1 month before surgical resection of the tumor; all enhanced examinations were of 3 stages, including arterial phase, portal venous phase, and Delayed period; clinical and imaging data are complete and available for re-evaluation.
[0114] Data exclusion criteria: Histopathological examination results were cholangiocarcinoma or mixed cell carcinoma; liver cancer interventional therapy or chemotherapy was performed before imaging examination; imaging data or clinical data were missing; tumors were not clearly displayed on imaging images, and lesions...
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The invention discloses a liver cancer image classification method and device combining computer vision features and radiomics features. The method includes the following steps: 1) collecting clinical images of patients meeting the standards, and preprocessing the collected images; 2) extracting computer vision features from the segmented images of liver tumor regions; 3) extracting computer vision features from the segmented images of liver tumor regions Carry out the extraction of manual features of radiomics; 4) Combining computer vision features and radiomics features, first pass through univariate filtering and then filter by LASSO regression; Model modeling, using the Akaike information criterion to search backward to select the clinical features that fit the best model, and realize the prediction of the pathological grade of liver cancer. The invention considers more dimensions and levels of image information, while maintaining the non-invasive, safe and stable advantages of radiomics, and is expected to become an effective tool for clinical preoperative evaluation of liver cancer.
Description
technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a non-invasive preoperative liver cancer pathological grading method combining computer vision features and radiomics features. Background technique [0002] Liver cancer is one of the important causes of tumor morbidity and death in the world, and China accounts for 50% of the world's new liver cancer cases. It endangers human life and health and causes a heavy economic burden to families and society. In the treatment of liver cancer, the degree of differentiation is an important factor affecting the prognosis of patients and the choice of liver transplantation strategy. The pathological grade is a means to express the degree of differentiation of liver cancer. Traditionally, the pathological grade of liver cancer can be obtained through tissue biopsy. However, the accuracy of tumor grading in tumor specimens obtained by biopsy is controversial, and...
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IPC IPC(8): G06K9/62G06K9/46G06K9/32G06T7/00G06T7/11G06T7/41G06V10/50G06V10/52
CPCG06T7/0012G06T7/11G06T7/41G06T2207/30056G06V10/446G06V10/50G06V10/467G06V10/25G06V2201/03G06F18/2135G06F18/241G06T2207/30096G06V10/52G06V10/462G06V2201/031G06V10/761G06F18/22G06V20/698G06V20/695
Inventor 丁勇阮世健邵嘉源戴悦阮翊婷
Owner ZHEJIANG UNIV