Lung segmenting device of 3D (three-dimensional) U-Net network based on mixed coarse segmentation features

Abstract

The invention discloses a lung segmenting device of a 3D (three-dimensional) U-Net network based on mixed coarse segmentation features. The lung segmenting device comprises a lung 3D binary image generating module, a lung segmenting module and an optimizing module, wherein the lung 3D binary image generating module is used for sequentially performing Gaussian filtering, binaryzation, marking of communication areas and screening, size matching and trimming on an original lung CT (computed tomography) slice so as to generate a lung 3D binary image; the lung segmenting module is used for calculating an original lung CT image and the lung 3D binary image by the trained 3D U-Net network, and outputting a segmenting probability map; the optimizing module is used for calculating the segmenting probability map by a conditional random field, and outputting the final lung segmenting result. The lung segmenting device has the advantage that while the higher segmenting accuracy is produced, the lower calculation consumption and memory consumption can be guaranteed.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a lung segmentation device based on a 3DU-Net network with mixed rough segmentation features. 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 feature parts in medical image data. At present, the CAD (computer aided diagnosis) 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 ...

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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 image, and the accuracy and speed of lung image segmentation in clinical applications are also very high, which has led to the lung image segmentation work becoming a medical A Dilemma of Imaging in Clinical Citation

[0004] Existing devices and methods for segmenting lung CT images are not accurate enough, and the segmentation efficiency cannot meet the needs

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