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Lumbar segment internal fixation mode simulation method and system based on deep learning

A lumbar segmental and deep learning technology, applied in the field of image processing and biomechanical simulation, can solve the problems of inability to explore mechanical properties, shortening of minimally invasive knife edges, etc., and achieve the effect of improving labor-intensive and accurate simulation

Pending Publication Date: 2022-06-24
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The inventor found that the clinical research index results show that the pedicle screw and CBT screw combined with double-segment internal fixation has better stability and greatly shortens the minimally invasive incision, but its mechanical properties cannot be explored

Method used

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  • Lumbar segment internal fixation mode simulation method and system based on deep learning
  • Lumbar segment internal fixation mode simulation method and system based on deep learning
  • Lumbar segment internal fixation mode simulation method and system based on deep learning

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Experimental program
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Embodiment 1

[0065] like Figure 1-Figure 4 As shown, this embodiment provides a deep learning-based simulation method for the internal fixation of lumbar vertebrae, including:

[0066] Obtain CT lumbar segmental scan images;

[0067] Segment the acquired CT lumbar segmental scan images to obtain vertebral body structure images;

[0068] Based on the segmented vertebral body structure, the 3D vertebral body model is reconstructed and preprocessed;

[0069] Based on the preprocessed 3D vertebral body model, add and assemble lumbar segmental components to obtain the fixed 3D vertebral body segment model;

[0070] Based on the 3D vertebral segment model, mesh its components;

[0071] Based on the meshed 3D vertebral body segment model, the finite element analysis was performed to obtain the simulation results of the internal fixation method of the lumbar vertebral segment.

[0072] Specifically, the method described in this embodiment includes the following steps:

[0073] S1: Medical im...

Embodiment 2

[0110] This embodiment provides a deep learning-based lumbar segment internal fixation simulation system, including:

[0111] an image acquisition module, configured to acquire CT lumbar segmental scan images;

[0112] The image segmentation module is configured to segment the acquired CT lumbar segmental scan images to obtain vertebral body structure images;

[0113] an image reconstruction module, configured to reconstruct a three-dimensional vertebral body model based on the segmented vertebral body structure and perform preprocessing;

[0114] The component assembly module is configured to add and assemble lumbar segmental components based on the preprocessed 3D vertebral body model to obtain a fixed 3D vertebral body segment model;

[0115] a meshing module configured to mesh its components based on the three-dimensional vertebral segment model;

[0116] The finite element analysis module is configured to perform finite element analysis based on the meshed three-dimensi...

Embodiment 3

[0121] This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements a deep learning-based simulation method for internal fixation of a lumbar spine segment as described in the first embodiment above steps in .

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Abstract

The invention provides a lumbar vertebra segment internal fixation mode simulation method and system based on deep learning. The method comprises the following steps: acquiring a CT lumbar vertebra segment scanning image; segmenting the obtained CT lumbar vertebra segment scanning image to obtain a vertebral body structure image; reconstructing a three-dimensional vertebral body model based on the segmented vertebral body structure and carrying out preprocessing; on the basis of the preprocessed three-dimensional vertebral body model, adding and assembling lumbar vertebra segment components to obtain a fixed three-dimensional lumbar vertebra segment model; on the basis of the three-dimensional lumbar segment model, carrying out grid division on the components of the model; carrying out finite element analysis on the basis of the three-dimensional lumbar segment model subjected to grid division to obtain a simulation result of a lumbar segment internal fixation mode; according to the method, the deep learning U-Net network is used for segmenting the image, the current situation that image annotation is troublesome and laborious is improved to a great extent, and a more accurate three-dimensional vertebral body model can be obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing and biomechanical simulation, and in particular relates to a deep learning-based simulation method and system for the internal fixation of lumbar vertebrae segments. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Spinal diseases include degenerative diseases of the lumbar spine, deformities, fractures, tumors, etc., and the onset patients are getting younger and younger, and they are common in the L4-L5 lumbar vertebrae. The clinical manifestations of patients are mostly pain in the lumbosacral region, lower limb pain caused by implication, and inability to stand upright. At present, for this disease, the clinical treatment mostly adopts internal fixation through "minimally invasive surgery", generally pedicle internal fixation and CBT internal fixati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/23G06F30/27G06T17/20G06V10/26G06V10/80G06V10/82G06K9/62G06N3/04A61B17/70G06F119/14G06F111/04
CPCG06F30/23G06F30/27G06T17/20A61B17/70G06F2119/14G06T2207/30012G06T2207/20084G06T2207/20112G06T2207/10081G06F2111/04G06N3/048G06N3/045G06F18/253
Inventor 华庆赵蒙蒙司海朋赵俊勇王晶晶
Owner SHANDONG NORMAL UNIV
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