Total hip replacement preoperative planning system based on deep learning

Abstract

The invention provides a total hip replacement preoperative planning system based on deep learning, and the system comprises a total hip joint image collection module which is used for obtaining a to-be-recognized total hip joint three-dimensional block graph; the total hip joint recognition module is used for inputting a to-be-recognized total hip joint three-dimensional block image into a trained three-dimensional segmentation neural network to obtain a thighbone area in each total hip joint two-dimensional cross section image, and the trained three-dimensional segmentation neural network is formed by a preset three-dimensional block image marked with a label of the thighbone area and a preset three-dimensional block image marked with a label of the thighbone area. The method is obtained by training a convolutional neural network. And the total hip joint three-dimensional image construction module is used for obtaining a three-dimensional image of a thighbone region according to the thighbone region in each total hip joint two-dimensional cross section image based on a three-dimensional reconstruction technology. According to the method, the total hip joint three-dimensional block graph is identified, three-dimensional modeling is carried out on the extracted thighbone area based on the three-dimensional block graph, and then the total hip joint identification precision is improved according to the thighbone area three-dimensional model.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a preoperative planning system for total hip replacement based on deep learning. Background technique [0002] Joint replacement refers to the use of materials such as metal, polymer polyethylene or ceramics to make artificial joint prostheses according to the shape, structure and function of human joints, and implant them into the human body through surgical techniques. [0003] In the preoperative planning of total hip replacement, physicians need to rely on their own experience to judge the entire femoral region from medical images of the hip joint, and determine the size and type of the prosthesis that needs to be replaced. However, traditional total hip replacement technology relies on physician experience, and physicians with different experience will have different identification results, making it difficult to guarantee the uniformity of results. ...

AI Technical Summary

Problems solved by technology

[0004] Since the shape of the hip joint is a three-dimensional structure, using a two-dimensional image segmentation neural network for total hip joint segmentation will lose the feature information between the continuous slice layers of the joint, resulting in low recognition accuracy of the total hip joint

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