Hybrid lung segmentation system based on deep learning and image processing

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...

AI Technical Summary

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 lung image segmentation. A difficult problem in clinical application

[0004] Existing devices and methods for segmenting lung CT images are all aimed at segmenting some organs of the lung, but the overall segmentation scheme required for pulmonary nodules in thoracic surgery has not been realized

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