Skeleton CT image three-dimensional segmentation method based on multi-view separation convolutional neural network

Abstract

The invention belongs to the technical field of image processing, provides a three-dimensional CT image segmentation method based on a multi-view separation convolutional neural network, and mainly relates to three-dimensional automatic segmentation of a skeleton in the CT image by using a novel convolutional neural network. The method aims to solve the problems that a neural network using three-dimensional convolution is too large in model, too high in running memory occupation amount and incapable of running on a small-video-memory-capacity display card or embedded device. Meanwhile, in order to improve the capability of the convolutional neural network for utilizing the three-dimensional space context information, a multi-view separation convolution module is introduced, the context information is extracted from the multi-view sub-images of a three-dimensional image by using a plurality of two-dimensional convolution, and the multi-level fusion is carried out, so that the extractionand fusion of the multi-view and the multi-scale context information are realized, and the segmentation precision of the skeleton in the three-dimensional CT image is improved. The average accuracy of the improved network structure is obviously improved, and the number of model parameters is obviously reduced.

Description

technical field [0001] The present invention relates to a bone CT scan image segmentation method, in particular to a three-dimensional bone CT image segmentation method based on a multi-view separation convolutional neural network. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. It is a key step from image processing to image analysis. Three-dimensional segmentation of bone CT images is a key and necessary task for orthopedic scientists to assist in the diagnosis of bone diseases. However, there is a large amount of redundant information in bone CT images, so it is very meaningful to efficiently screen out the most effective feature indicators. However, manually delineating such very large-scale, high-resolution data is time-consuming, tedious, and has limited reproducibility. And most existing automatic segmentation methods rely on a ...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to solve the problems existing in the existing models, such as slow convergence speed, too large model, high running memory usage, and inability to ru

Images