Lung lobe segmentation system based on three-dimensional convolutional neural network

A three-dimensional convolution and neural network technology, applied in the field of image processing, can solve the problems of not meeting the daily work needs of doctors and low execution efficiency

Active Publication Date: 2021-04-20
SHAN DONG MSUN HEALTH TECH GRP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The inventors found that currently many segmentation algorithms are commonly used in the segmentation of blood vessels and lungs. Most of the algorithms for lung lobe segmentation use traditional geometric methods, which have low execution efficiency and cannot meet the daily work needs of doctors.

Method used

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  • Lung lobe segmentation system based on three-dimensional convolutional neural network
  • Lung lobe segmentation system based on three-dimensional convolutional neural network
  • Lung lobe segmentation system based on three-dimensional convolutional neural network

Examples

Experimental program
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Embodiment 1

[0046] Such as figure 1 , figure 2 with image 3 As shown, Embodiment 1 of the present disclosure provides a lung lobe segmentation system based on a three-dimensional convolutional neural network, including steps:

[0047] The data acquisition module is configured to: acquire chest CT image data to be identified, and perform preprocessing;

[0048] The lung lobe segmentation module is configured to: input the preprocessed lung CT image data into a preset three-dimensional convolutional neural network model to obtain a lung lobe segmentation result.

[0049] Specifically, include the following:

[0050] S1: Select the appropriate chest CT data, and expert doctors will carry out the data labeling work, mainly through the 3DSlicer software, the 5 lung lobes of the lungs are drawn along the edges with different colors, including: left upper lobe, The lower lobe of the left lung, the upper lobe of the right lung, the middle lobe of the right lung, and the lower lobe of the ri...

Embodiment 2

[0067] Embodiment 2 of the present disclosure provides a computer-readable storage medium on which a program is stored, and when the program is executed by a processor, the following steps are implemented:

[0068] Obtain chest CT image data to be identified and perform preprocessing;

[0069] Input the preprocessed lung CT image data into the preset three-dimensional convolutional neural network model to obtain the lung lobe segmentation result;

[0070] Among them, the three-dimensional convolutional neural network model includes three down-sampling layers and three up-sampling layers, the down-sampling layer is realized by the convolution layer, and the up-sampling layer is realized by the deconvolution layer.

[0071] The detailed steps are the same as the method provided in Embodiment 1, and will not be repeated here.

Embodiment 3

[0073] Embodiment 3 of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and operable on the processor, and the processor implements the following steps when executing the program:

[0074] Obtain chest CT image data to be identified and perform preprocessing;

[0075] Input the preprocessed lung CT image data into the preset three-dimensional convolutional neural network model to obtain the lung lobe segmentation result;

[0076] Among them, the three-dimensional convolutional neural network model includes three down-sampling layers and three up-sampling layers, the down-sampling layer is realized by the convolution layer, and the up-sampling layer is realized by the deconvolution layer.

[0077] The detailed steps are the same as the method provided in Embodiment 1, and will not be repeated here.

[0078] Those skilled in the art should understand that the embodiments of the present disclosure may be pr...

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Abstract

The invention provides a lung lobe segmentation system based on a three-dimensional convolutional neural network, and the system comprises a data obtaining module which is configured to obtain to-be-recognized chest CT image data and carry out the preprocessing of the to-be-recognized chest CT image data; the lung lobe segmentation module is configured to input the preprocessed lung CT image data into a preset three-dimensional convolutional neural network model to obtain a lung lobe segmentation result; wherein the three-dimensional convolutional neural network model comprises three lower sampling layers and three upper sampling layers, the lower sampling layers are realized through convolution layers, and the upper sampling layers are realized through deconvolution layers. According to the method, segmentation of different lung lobe regions can be rapidly realized, and the method has relatively high robustness for wide clinical data and relatively high generalization for data of different hospitals.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, in particular to a lung lobe segmentation system based on a three-dimensional convolutional neural network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] With the continuous improvement of medical hardware equipment, the quality of CT images is getting higher and higher, and the number of images has also doubled. Moreover, due to the volume effect, the patient's body shaking and other factors during the shooting process, the CT image is blurred, and it is difficult for the doctor to judge which lung lobe the lesion is located in, which brings great difficulties to the speed and accuracy of the doctor's diagnosis. Bring a certain burden to the doctor's daily work. [0004] In chest CT images, different locations of lesions in lung lobes h...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06N3/04G06N3/08
Inventor 樊昭磊吴军尚永生颜世舵王剑
Owner SHAN DONG MSUN HEALTH TECH GRP CO LTD
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