Static artery separation method and device based on CT image

Abstract

The invention discloses a static artery separation method and device based on a CT image, and the method and device improve the efficiency and precision of static artery separation compared with a conventional method and manual labeling of a doctor, achieve the full-automatic static artery separation, and do not need the manual intervention. The method mainly comprises the following steps: carrying out lung region segmentation on a chest CT image by using a preset three-dimensional lung segmentation model to obtain a three-dimensional mask of a lung region; carrying out convex hull operation on a lung mask, taking a lung region according to the lung mask subjected to convex hull operation, setting the pixel value of the extrapulmonary region to be 0, and obtaining a maximum extrapulmonarybounding box according to the lung mask; and performing static artery separation on the segmented CT image of the lung in the lung external bounding box by using a preset downsampling-free three-dimensional cavity convolutional neural network to obtain a static artery mask. As the accurate annotation data is used for training, and the three-dimensional cavity convolutional neural network is used for learning, the loss of information amount is reduced, and the accurate separation of the static artery is realized.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a method and device for separating veins and arteries based on CT images. Background technique [0002] Over the past few decades, computed tomography (CT) has become the most common imaging modality for diagnosing and evaluating lung disease. Modern CT scanners combined with modern imaging techniques allow (semi)automatic identification and extraction of lung structures, such as blood vessels and bronchi, with high accuracy using low radiation doses. However, although some progress has been made in CT image segmentation techniques in recent years, there are still many problems that have not been resolved. Among them, the identification and differentiation of pulmonary arteries and pulmonary veins is one of the most challenging problems. [0003] Dividing the pulmonary vessels into arteries / veins (A / Vs) may help doctors accurately diagnose lung diseases that may affect t...

AI Technical Summary

Problems solved by technology

This process is time-consuming and difficult to standardize, making it unsuitable for large clinical studies or clinical decision-making in the real world

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