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Method and system, storage medium and electronic equipment for identifying force lines using neural network

A neural network and recognition technology, applied in the field of medical technology, can solve problems such as patient lesions and surgical failures

Active Publication Date: 2022-05-13
LANCET ROBOTICS CO LTD
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Problems solved by technology

If the calibration of the force line is inaccurate, it may cause the patient to cause lesions again during the postoperative recovery process, and even lead to the complete failure of the entire operation

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  • Method and system, storage medium and electronic equipment for identifying force lines using neural network
  • Method and system, storage medium and electronic equipment for identifying force lines using neural network

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Embodiment Construction

[0021] Exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The exemplary embodiments described below and illustrated in the accompanying drawings are intended to teach the principles of the invention and enable those skilled in the art to implement and use the invention in a number of different environments and for a number of different applications. Therefore, the protection scope of the present invention is defined by the appended claims, and the exemplary embodiments are not intended and should not be considered as a limiting description of the protection scope of the present invention.

[0022] refer to Figure 9 According to the present invention, there is provided a method and system for segmenting femur and tibia images using deep learning and determining the position of force lines according to medical definitions on this basis, so as to improve the accuracy of subsequent operations. The specific...

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Abstract

A method and system for assisting identification of force lines using a neural network, which can be used to detect force lines before and after surgery, assist surgery and understand postoperative recovery status. The method comprises the steps of: obtaining a first image by combining lines of CT file sequences into a complete three-dimensional image, and then performing slices from the coronal plane, and inputting the first image into a first multi-class segmentation based on unet and using a softmax activation function The neural network classifies the femur and tibia differently, thereby segmenting the models of the femur and tibia at one time; and using the second segmentation to find the center point of the femoral head on the femoral model with the neural network to determine the line of force, which is also based on the point cloud Calculate other key physiological points: the center point of the femoral knee joint, the center point of the tibial knee joint and the center point of the tibial ankle joint. Wherein, bilinear quantization processing is performed on the first image, so that down-quantization can also be performed while upward interpolation is performed through bilinear interpolation.

Description

technical field [0001] The present invention relates to the field of medical technology, that is, the field of computer-aided planning technology for joint replacement and the technical field of medical image data processing. More specifically, it involves a method based on image reconstruction, deep learning, and numerical algorithms to calibrate the line of force, especially redesigning a different deep learning marking scheme based on the coronal plane. Background technique [0002] With the rapid development of modern society, all walks of life have begun to have an inseparable connection with the IT industry, and the same is true for the medical industry. Determining the position of the force line is very important in assisted knee arthroplasty by surgical robot, which can determine the success of the operation and the postoperative recovery status of the patient. If the calibration of the force line is not accurate, it may cause the patient to re-introduce the lesion ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T17/00G06V10/26G06V10/764G06V10/82G06K9/62G06N3/04A61B34/10A61B34/30
CPCG06T7/0012G06T17/00A61B34/10A61B34/30G06T2207/10081G06T2207/30008A61B2034/101A61B2034/107A61B2034/105G06N3/045G06F18/2431
Inventor 黄志俊刘金勇钱坤范昕
Owner LANCET ROBOTICS CO LTD
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