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CT image lung lobe recognition method based on neural network

A CT imaging and neural network technology, applied in the field of computer-aided medicine, can solve problems such as errors, achieve high accuracy, high feature learning ability, and increase the complexity of the model

Inactive Publication Date: 2020-03-31
SHAN DONG MSUN HEALTH TECH GRP CO LTD
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Problems solved by technology

These methods are only feasible under extremely ideal conditions, and will lead to large errors in the case where there is a lesion near the fissure causing the fissure to be blurred

Method used

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Embodiment Construction

[0032] The present invention will be further described below.

[0033] A neural network-based CT image lung lobe recognition method, comprising the steps of:

[0034] a) Collect chest CT image data, and label the chest CT data according to background, left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe

[0035] A well-converged lung lobe recognition model is obtained through a convolutional neural network, and the specific methods are shown in step b)-step r).

[0036] b) Input the chest CT image X into the residual module C 1 , using the computer through the residual module C 1 The feature output map C is obtained after compound operation processing 1 (X)

[0037] c) Use the computer to output the features in Figure C 1 (X) Perform maximum pooling operation, compress feature map C1 (X), get the updated feature output map C' 1 (X). By compressing the feature map C 1 (X) allows the network to learn the characteristics of position ...

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Abstract

A CT image lung lobe recognition method based on a neural network is a difficult task in the field of traditional image processing due to the fact that lung fissure and lung parenchyma are low in CT value discrimination. By means of the CT image lung lobe recognition method based on the neural network, the network can learn more complex and high-dimensional features by means of the enough deep dimension and enough parameter quantity of the CT image lung lobe recognition method. Due to the introduction of the residual module, the complexity of the model is increased, and the model is endowed with higher feature learning capability. On the basis of the high feature extraction capability of the original U-Net, the subsequent multi-scale fusion channel can enable the model to learn the features of different lung lobe boundaries from coarse to fine. Therefore, the high-accuracy lung lobe recognition model is realized.

Description

technical field [0001] The invention relates to the field of computer-aided medicine, in particular to a neural network-based CT image lung lobe recognition method. Background technique [0002] Lobe identification is a necessary step in the assessment of lung diseases. Different lesions are located in different lobes, and the possible disease grades will be different. The current traditional method for identifying lung lobes is to first identify the lung lobes through traditional digital image processing algorithms, and then locate each lung lobe according to the relative position of the lung area and the pulmonary fissure. These methods are only feasible under extremely ideal conditions, and in the case that there is a lesion near the fissure that causes the fissure to be blurred, it will cause a large error. Contents of the invention [0003] In order to overcome the deficiencies of the above technologies, the present invention provides a neural network-based method fo...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10081G06T2207/20084G06T2207/30061G06T2207/30204
Inventor 曲荣芳吴军樊昭磊颜红建尚永生
Owner SHAN DONG MSUN HEALTH TECH GRP CO LTD
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