Navigation method and system for lung biopsy bronchoscope

A technology of bronchoscopy and navigation methods, applied in neural learning methods, computer components, character and pattern recognition, etc., to achieve the effects of reducing the incidence of complications, improving detection accuracy, and shortening inspection time

Pending Publication Date: 2021-07-13
TONGJI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, there are no relevant researches and plans on applying the surgical navigation system to the interventional operation bronchoscope of the small bronchi in the existing technology.

Method used

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  • Navigation method and system for lung biopsy bronchoscope
  • Navigation method and system for lung biopsy bronchoscope
  • Navigation method and system for lung biopsy bronchoscope

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Embodiment Construction

[0068] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0069] Such as figure 1 As shown, the present embodiment provides a navigation method for a novel lung biopsy bronchoscope, comprising the following steps:

[0070] Step S1, obtaining CT image data:

[0071] Step S11, performing a lung image parenchyma extraction operation on the initial CT image data to obtain an intermediate data set;

[0072] Step S12, preprocessing the intermediate data set to obtain the final CT image data: first read the original image of the dicom file, and extract the pixel values; normalize the extracted pixel values, and the normalization formula is:

[0...

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Abstract

The invention relates to a navigation method and system for a lung biopsy bronchoscope. The method comprises the following steps: acquiring CT image data; inputting the CT image data into the trained deep learning pulmonary nodule recognition neural network model, and outputting to obtain pulmonary nodule data; performing endoscope image space three-dimensional model reconstruction on the CT image data by using a virtual endoscope technology, and performing focus marking in an endoscope image space three-dimensional model in combination with pulmonary nodule data; selecting a focus mark in the endoscope image space three-dimensional model, and performing operation path planning; and mapping the pose of the bronchoscope catheter from the actual physical coordinate system to an endoscope image space, and carrying out registration from the actual physical position space to the image space. Compared with the prior art, the invention has the advantages of integration of deep learning nodule diagnosis, CT reconstruction and virtual endoscope navigation, accurate positioning, high diagnosis efficiency and the like.

Description

technical field [0001] The invention relates to the technical field of bronchoscopy, in particular to a navigation method and system for lung biopsy bronchoscopy. Background technique [0002] Bronchial lung cancer (referred to as lung cancer) is a malignant tumor with the highest morbidity and mortality in the world. In recent years, the morbidity and mortality have been increasing year by year. Surgery is still the most effective treatment for lung cancer at present. However, due to the non-specific clinical manifestations of early lung cancer, most patients have missed the opportunity for surgery when they are diagnosed with lung cancer, and the overall effect of current chemotherapy and radiotherapy is not ideal. Early diagnosis and surgery are the key to improving the survival of lung cancer patients, so how to accurately identify and diagnose lung cancer technology at an early stage is very important. [0003] At present, the commonly used method for early screening o...

Claims

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Application Information

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IPC IPC(8): G06T19/00G06T17/00G06T7/73G06T7/33G06N3/08G06N3/04G06K9/62
CPCG06T19/003G06T17/00G06T7/73G06T7/33G06N3/08G06T2207/10081G06T2207/20132G06T2207/30064G06T2207/20081G06N3/045G06F18/2414
Inventor 齐鹏任纪媛李雨晗
Owner TONGJI UNIV
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