Urinary calculus detection and classification method based on deep learning and imaging omics

Abstract

The invention relates to a urinary calculus detection and classification method and system based on deep learning and imaging omics. The method comprises the following steps: firstly, automatically extracting a calculus region of interest from a CT image by utilizing a first deep learning model; and carrying out existence confirmation and primary rough classification on the stone region of interest, and carrying out secondary fine classification on the detected stone region of interest by utilizing a second deep learning model and the extracted iconomics characteristics around the stone in thestone region of interest to obtain a final stone classification result. According to the invention, a plurality of deep learning models and characteristics of calculus imaging omics are combined andutilized, so that an efficient, accurate and full-process automatic calculus classification method is realized, and support is provided for clinical calculus treatment of a subsequent process.

Description

technical field [0001] The present invention relates to an image processing technology, in particular to a method and system for detecting and classifying urinary stones based on deep learning and radiomics. Background technique [0002] Urinary calculus is a common clinical disease, with a prevalence rate of 5% to 10%. According to the composition, it can be divided into calcium phosphate, calcium oxalate, magnesium amine phosphate, cystine, and uric acid stones. According to the 2019 guidelines of the European Urological Association (EUA), there are differences in the treatment of stones with different components. Uric acid stones can be treated by uric acid-lowering and other drugs to dissolve stones. ESWL is resistant and not easily crushed. Preoperative non-invasive detection and classification of urinary calculi is crucial to the selection of treatment options. [0003] At present, the commonly used method to distinguish the composition of stones in clinic is to mak...

AI Technical Summary

Problems solved by technology

This method also uses manual screening of the region of interest, which cannot be automatically identified and extracted

[0006] It can be seen from the above that how to automatically find the region of interest where the stone is located in the CT image and extract the region of interest is an urgent technical problem at present. At present, the sources of CT image equipment are different, and different parameter settings will lead to differences in the image. , the robustness of the general algorithm is poor

In addition, in the process of stone detection, it is difficult to effectively train the general model when there are few labeled image data, which makes the classification effect of urinary stones inaccurate.

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