CT image lung trachea segmentation method and system based on deep learning

Abstract

The embodiment of the invention provides a CT image lung and trachea segmentation method and system based on deep learning, and the method employs a 2D UNet and 3D Unet deep learning network model atthe same time, and comprises the following steps: S1, preprocessing; s2, carrying out two-dimensional resampling; s3, carrying out two-dimensional segmentation; s4, performing three-dimensional sampling; s5, carrying out three-dimensional segmentation; and S6, fusion: carrying out combination operation on the two-dimensional and three-dimensional segmentation results of the lung trachea to obtaina fused segmented lung trachea, and then taking the maximum three-dimensional connected region in the calculation image as a final lung trachea segmentation result. According to the CT image lung trachea segmentation method and system based on deep learning, a 2D UNet network and a 3D Unet network are used at the same time, so that the trachea segmentation result is better, and the segmentation method is more robust. In the 3D UNet network training process, the training efficiency and precision of the network can be improved by a tracheal skeleton point sampling method.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to a method and system for segmenting lung and trachea in CT images based on deep learning. Background technique [0002] Lung cancer is the most threatening tumor to human life and health. Early detection is crucial to the survival and recovery of patients. Clinically, pulmonary nodule detection is the first step in lung cancer screening. Through the detection and segmentation of lung trachea and blood vessels in CT images, it is of great significance to the early screening and evaluation of lung cancer. Due to the complexity and individual differences of body tissues and organs, coupled with the differences between different imaging devices, the results of medical imaging are very complicated, resulting in the problem of inaccurate segmentation results of traditional medical image segmentation methods. [0003] In the field of lung and trachea segmentati...

AI Technical Summary

Problems solved by technology

Due to the complexity and individual differences of body tissues and organs, coupled with the differences between different imaging devices, the medical imaging results are very complicated, resulting in the inaccurate segmentation results of traditional medical image segmentation methods.

[0003] In the field of lung and trachea segmentation in the field of medical image segmentation, the imaging of lung and trachea has low contrast, the structure of small blood vessels and trachea is complex, and the lung image is noisy, which makes accurate lung and trachea segmentation difficult. , most of them are divided manually, which is not only inefficient, but also the accuracy varies from person to person, so it is difficult to guarantee

[0004] In recent years, artificial intelligence technology, especially the deep learning method, has developed very rapidly and has been widely used in the field of medical image segmentation. However, as far as the current public methods are concerned, the accuracy of lung and tracheal segmentation still needs to be further improved.

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