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A lung lobe segmentation method based on digital human technology

A digital human and lung lobe technology, applied in image analysis, image data processing, image enhancement and other directions to achieve the effect of improving accuracy and stability

Active Publication Date: 2021-06-01
ZHEJIANG LAB
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Some scholars have designed the VanderBurg linear operator for the structural features of lung fissures in two-dimensional space to detect lung fissures, but this method can only be applied to lung images without lesions. Combining to improve the stability and precision of lung fissure detection

Method used

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  • A lung lobe segmentation method based on digital human technology
  • A lung lobe segmentation method based on digital human technology
  • A lung lobe segmentation method based on digital human technology

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0029] Such as figure 1 It is a schematic diagram of a lung lobe segmentation method based on digital human technology, and the method includes the following steps:

[0030] Step 1: Generate a lung digital human model containing lung lobe mask information through registration and data statistics of lung CT images, specifically including the following sub-steps:

[0031] (1.1) Each collected lung CT image was preprocessed with threshold division for lung lobe segmentation, and then the interactive segmentation software was used for precise lung lobe segmentation.

[0032] (1.2) Surface registration is performed between each segmented lung CT image and the template, and the registered boundary point cloud data is obtained as a sample set. This process often uses non-rigid point cloud registration algorithms, such as TPS-RPM and non-rigid ICP.

[0033] (1.3) Construct a digital demographic map and set a sample set:

[0034] Ω={ X 1 , X 2 ,…, X N} (1)

[0035] i-th sampl...

Embodiment 2

[0066] As a preferred solution, the present invention can also adopt the elastix function in the Elastix toolkit to obtain the deformation field of the digital human image and the clinical patient's lung CT image and the deformed digital human image, specifically:

[0067] A lung lobe segmentation method based on digital human technology, the method comprises the following steps:

[0068] Step 1: Using the elastix function in the Elastix toolkit, the digital human image generated by the digital human model constructed in Example 1 and the clinical lung CT image of the patient are used as input to obtain the deformation field and the deformed digital human image.

[0069] Step 2: Using the digital human image deformed in Step 1, use the trained encoding network ( Figure 4 ) to fit the shape parameters of the digital human, and generate a new digital human image according to the shape parameters of the digital human.

[0070] Step 3: Repeat step 1 and step 2 multiple times unt...

Embodiment 3

[0074] As another preferred solution, the present invention can also connect the trained non-rigid registration model to the encoding network, and output the fitted shape parameters in one step, as follows:

[0075] A lung lobe segmentation method based on digital human technology, the method comprises the following steps:

[0076] Step 1: The digital human image generated by the digital human model constructed in Example 1 and the clinical lung CT image of the patient are used as the input of the joint network composed of the non-rigid registration model and the encoding network connection to obtain the deformed digital human shape parameters, and generate a new digital human image according to the shape parameters of the digital human.

[0077] Step 2: Repeat step 1 for several times until the iteration threshold is reached or the shape parameters of the digital human converge, and a new digital human image generated after final fitting is obtained.

[0078] Step 3: The new...

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Abstract

The invention discloses a lung lobe segmentation method based on digital human technology. The method performs non-rigid registration on a digital human image and a clinical patient's lung image to obtain a deformation field and a deformed digital human image. The digital human image fits the shape parameters of the digital human and generates a new digital human image according to the shape parameters, and then iteratively registers and updates the new digital human image and the patient's lung image to obtain a shape closer to the patient's lung image. Finally, the digital human image is non-rigidly registered with the patient’s lung image to obtain the deformation field, and the deformation field is added to the boundary point cloud or mask image of the digital human lung lobe, and the result obtained by this method is The result of lung lobe segmentation. The method of the present invention utilizes the digital human model for the first time to perform organ segmentation in medical images, and the method of the present invention can effectively improve the accuracy and stability of lung lobe segmentation in the case of abnormalities or lesions in patient images.

Description

technical field [0001] The invention relates to the field of image segmentation, in particular to a lung lobe segmentation method based on digital human technology. Background technique [0002] Lung lobe segmentation is a method of obtaining lung lobe boundary information through image segmentation. It is an important prerequisite for lung visualization and lung quantitative analysis, and plays a very important role in the early diagnosis and treatment of lung cancer. In clinical practice, the functions of lung lobes are relatively independent and lung diseases usually occur in a single lung lobe. Accurate lung lobe segmentation is the premise of many lung operations (such as lung lobe reduction surgery). The lung lobes are separated by fissures, but in practical applications, the segmentation of lung lobes is affected by factors such as limited CT resolution, incomplete pulmonary fissures, distribution of abnormal structures around, and abnormal lung parenchyma. difficult...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/33G06T7/60
CPCG06T7/0012G06T7/11G06T7/344G06T7/60G06T2207/10028G06T2207/30061G06T2210/44
Inventor 朱闻韬饶璠张铎
Owner ZHEJIANG LAB