Lung CT image parameter reconstruction method and system based on deep learning, terminal and storage medium

Abstract

The invention provides a lung CT image parameter reconstruction method and system based on deep learning, a terminal and a storage medium. The method comprises the steps: inputting an obtained lung CTimage into a preset progressive up-sampling skeleton network model, and outputting a feature image; inputting the feature image into a 3D convolutional neural network model, wherein the 3D convolutional neural network model is a convolutional neural network model composed of an intra-pulmonary branch, an extra-pulmonary branch and a pulmonary mask branch; classifying each pixel of the feature image through the lung mask branches, determining an intrapulmonary part and an extrapulmonary part, and performing classification marking; respectively inputting the intra-pulmonary part and the extra-pulmonary part of the feature image into the intra-pulmonary branch and the extra-pulmonary branch for feature learning to generate an intra-pulmonary image and an extra-pulmonary image; combining thegenerated images of the intrapulmonary branches and the extrapulmonary branches to form a complete CT generated image; conversion of different parameters of the CT image is realized by using a deep learning technology.

Description

technical field [0001] The present application relates to the field of medical imaging and computer-aided technology, and in particular to a method, system, terminal and storage medium for reconstructing lung CT image parameters based on deep learning. Background technique [0002] Lung CT is currently the most important method for detecting lung diseases. In actual clinical applications, doctors will set the parameters of scanning CT according to different situations. Different parameters will directly affect the imaging quality and imaging time of the final CT. For example, CT different slice thicknesses and interslice distances, different reconstruction algorithms and scanning doses, etc. [0003] Generally, CT with slice thickness less than or equal to 2 mm is called thin slice CT, and slice thickness greater than 2 mm is called thick slice CT. The advantage of thin-section CT is that it has higher image resolution, can clearly observe the coronal and sagittal lesions, ...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the deficiencies of the prior art, this application provides a lung CT image parameter reconstruction method, system, terminal and storage medium based on deep learning, which solves the problem that the CT image reconstruction of a single parameter in the prior art cannot meet the needs and different parameter CT Problems such as the inability to convert images to each other

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