Urinary calculus detection and classification method based on deep learning and imaging omics
A deep learning and radiomics technology, applied in the field of image processing, can solve the problems of poor algorithm robustness, different sources of CT image equipment, and inability to achieve automatic identification and extraction, so as to overcome errors and improve precision and accuracy. Effect
Pending Publication Date: 2020-06-26
JIANGXI PROVINCIAL PEOPLES HOSPITAL +1
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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.
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