Hybrid lung segmentation system based on deep learning and image processing

A technology of deep learning and image processing, applied in the field of image processing, can solve problems such as different quality requirements and failure of the overall segmentation scheme to achieve the effect of improving accuracy and robustness

Active Publication Date: 2020-12-11
杭州微引科技有限公司
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Problems solved by technology

However, due to the large individual differences in the internal tissues of the human body, different algorithms have different requirements on the shape and quality of the input images, and the accuracy of lung image segmentation in clinical applications is also very high, resulting in lu...

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  • Hybrid lung segmentation system based on deep learning and image processing

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[0014] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0015] Embodiments of the present invention relate to a hybrid lung segmentation system based on deep learning and image processing, such as figure 1 As shown, it includes: an acquisition module, a preprocessing module, a first segmentation module, a second segmentation module and a third segmentation module.

[0016] Wherein, the obtaining module is used to obtain the DICOM file of the lung CT image;

[0017] The preprocessi...

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Abstract

The invention relates to a hybrid lung segmentation system based on deep learning and image processing. The system comprises an obtaining module which is used for obtaining a DICOM file of a lung CT image, a preprocessing module which is used for preprocessing the DICOM file, a first segmentation module which is used for carrying out bronchial segmentation, blood vessel segmentation and pulmonarynodule detection on the preprocessed DICOM file, a second segmentation module which is used for taking the DICOM file, the bronchial segmentation result and the blood vessel segmentation result as input of a deep learning model to perform lung lobe segmentation and extraction and arteriovenous segmentation, and a third segmentation module which is used for performing lung segment segmentation by taking the bronchial segmentation result, the pulmonary lobe segmentation result and the arteriovenous segmentation result as input of a segmentation model. According to the invention, the segmentationof the whole lung is realized, and the anatomical structure reference of preoperative planning can be provided for a lung wedge-shaped operation or a puncture ablation operation.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a hybrid lung segmentation system based on deep learning and image processing. Background technique [0002] Deep learning methods have made great achievements in the field of image processing, which also provides the possibility to apply deep learning technology to identify special parts of medical image data. At present, the CAD system based on deep learning has a wide range of applications in identifying and segmenting organs and feature regions in CT images. [0003] As a branch of image processing, image segmentation is an important research direction in the medical field. The two-dimensional reconstruction and quantitative analysis of human tissue need to segment the relevant parts in advance. However, due to the large individual differences in the internal tissues of the human body, different algorithms have different requirements on the shape and quality of the...

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Application Information

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IPC IPC(8): G06T7/136G16H30/20G06K9/62G06N3/08
CPCG06T7/136G06N3/08G16H30/20G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30064G06T2207/30101G06F18/23G06F18/2411
Inventor 张忞周欣欢刘艺博
Owner 杭州微引科技有限公司
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