Lung lobe segmentation method and device based on UNet network and computer readable storage medium

Abstract

The invention provides a lung lobe segmentation method and device based on a UNet network and a computer readable storage medium, and relates to the field of lung lobe image processing. The lung lobesegmentation method comprises the following steps: acquiring lung CT image data from an image input device; carrying out normalization processing on the input lung CT image data; screening out an intra-pulmonary region and an extra-pulmonary region from the processed image data by utilizing a 2D UNet network, and taking the intra-pulmonary region as a lung region candidate region; dividing the lung region candidate region into five lung lobe mask regions by using a 3D UNet network to obtain regions of a left upper lobe, a left lower lobe, a right upper lobe, a right middle lobe and a right lower lobe; respectively carrying out morphological processing on the five lung lobe mask regions to obtain a final lung lobe segmentation result; and storing the lung lobe segmentation result in a memory or outputting and displaying the lung lobe segmentation result on a screen of a display. The lung lobe is quickly and accurately extracted through the UNet network, the lung cancer position is positioned, and guidance is provided for doctors to diagnose and treat lung cancer.

Description

technical field [0001] The invention relates to the technical field of lung lobe image processing, in particular to a UNet network-based lung lobe segmentation method, device and computer-readable storage medium. Background technique [0002] With the popularization of CT application, it provides convenience for the early screening of lung cancer. Statistics in recent years have found that the incidence of lung cancer is getting higher and higher, and it is also the primary cause of cancer mortality. Surgical resection of lung lobes is the primary method for the treatment of lung cancer. Traditional lung lobe segmentation algorithms generally use grayscale information to find lung fissures, and then extract lung lobes based on lung fissures and other anatomical information (trachea and blood vessels). , blurred, it is difficult to find all the lung fissures in all slices, resulting in difficulty in extracting lung lobes. How to quickly and accurately extract the lung lobes...

AI Technical Summary

Problems solved by technology

[0003] The main purpose of the present invention is to provide a lung lobe segmentation method, device and computer-readable storage medium based on the UNet network, aiming at solving the probl

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