Intelligent detection and early warning method and system for outer envelope in prostate operation

Abstract

The invention discloses an intelligent detection and early warning method and system for an outer envelope in a prostate operation. The method comprises the following steps: 1) acquiring outer envelope image data in a prostate operation video; 2) carrying out gray processing and singular value decomposition on the outer envelope data, and extracting a principal component characteristic value of the image; 3) performing image enhancement on the outer coating image after the image preprocessing in the first step by adopting a deep bilateral learning method; 4) neural network training; 5) detection and early warning. According to the invention, the latest artificial intelligence image recognition technology is utilized to carry out target detection on the prostate envelope, so that an intelligent early warning function is provided for the operations. Different from the existing static medical image recognition technology, the method is characterized in that dynamic images of an operationsite video need to be recognized, early-warned and analyzed. By means of image preprocessing measures such as data expansion, principal component analysis and image enhancement, speed and precision are balanced, and the actual application requirements of prostate operation auxiliary early warning are met.

Description

Technical field [0001] The present invention relates to the technical field of artificial intelligence target detection, in particular to a method and system for intelligent detection and early warning of outer membrane in prostate surgery. Background technique [0002] In the field of traditional image processing, target detection is a very popular key technology, and more researches include face detection and pedestrian detection. Traditional target detection generally uses a sliding window framework, which mainly includes three steps: one is to use the sliding window to select candidate regions; the other is to extract visual features related to the candidate regions; the third is to use classifiers for recognition. The more classic algorithm is a multi-scale deformable component model. This algorithm can be regarded as an extension of the "gradient histogram + support vector machine" method. The disadvantage is that it is more complex, slow in operation, and cannot support ap...

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Problems solved by technology

[0005] Aiming at the specific needs of early warning analysis and application of minimally invasive plasma bipolar resection surgery and the development status of medical image processing technology, the present invention proposes a method and system for intelligent detection and early warning of the outer capsule in prostate surgery, focusing on solving two problems : First, the real-time guarantee of the outer capsule detection based on the video image of the surgical site; second, on the premise of ensuring that there is no missed detection, try to improve the accuracy of the location of the outer capsule and provide surgeons with better early warning instructions and help

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