Fully-automatic three-dimensional liver segmentation method based on local apriori information and convex optimization

Abstract

The invention relates to medical image processing, and aims to provide a fully-automatic three-dimensional liver segmentation method based on local apriori information and convex optimization. The fully-automatic three-dimensional liver segmentation method based on the local apriori information and the convex optimization comprises the following steps: processing abdominal liver CTA volume data by utilizing a trained three-dimensional convolutional neural network, and then obtaining an aprior probability graph of a liver; obtaining an initial region of the liver from the aprior probability graph of the liver; determining probabilities of various pixel points, belonging to a foreground liver and a background, in an image; optimizing a new energy model by utilizing a convex optimization technology, and segmenting the liver; and performing post-processing, and then obtaining a contour of the liver. The method obtains a segment result. The method can overcome problems of under segmentation and inaccurate boundaries, which exist in an original liver segmentation by utilizing a convolutional neural network, well, and then can obtain an accurate segmentation result.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a fully automatic three-dimensional liver segmentation method based on local prior information and convex optimization. Background technique [0002] At present, liver disease is a disease with a relatively high incidence rate clinically, which directly threatens people's lives, so the accurate diagnosis of liver disease has important medical significance. Clinically, doctors often use a CT machine, that is, a computerized tomography machine, to obtain a series of planar grayscale tomographic images of the liver, and to judge the lesion location, characteristics, size, and surrounding tissue of the lesion by continuously viewing these images. relationship between etc. The extraction and quantitative analysis of the liver play a key role in accurately diagnosing liver diseases and formulating appropriate surgical plans. Clinically, the extraction of the liver is often out...

AI Technical Summary

Problems solved by technology

However, since the shape and position of the liver vary greatly among different individuals, the liver segmentation results obtained through the convolutional network will appear under-segmented in the left lobe of the liver.

In addition, the segmentation accuracy of the liver edge is not high

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