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A liver automatic segmentation method of abdominal CT sequence images based on a level set and a shape descriptor

A shape description and sequence image technology, applied in the field of medical image analysis and processing, can solve the problems of noise sensitivity, unsatisfactory low-contrast CT image segmentation results, and inability to accurately segment the liver, so as to optimize the liver edge and improve the liver segmentation accuracy.

Active Publication Date: 2018-12-11
湖南提奥医疗科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional methods are sensitive to noise, and the segmentation results of low-contrast CT images are not ideal
Shape model-based methods fail to accurately segment abnormally shaped livers
Liver segmentation based on deep learning requires a large amount of data as support during network training, which takes a long time to train and requires high hardware requirements

Method used

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  • A liver automatic segmentation method of abdominal CT sequence images based on a level set and a shape descriptor
  • A liver automatic segmentation method of abdominal CT sequence images based on a level set and a shape descriptor
  • A liver automatic segmentation method of abdominal CT sequence images based on a level set and a shape descriptor

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Embodiment 1

[0032] figure 1 Shown is a flow chart of a method for automatic liver segmentation of abdominal CT sequence images based on level sets and shape descriptors according to an embodiment of the present invention. Firstly, the input abdominal CT sequence image is preprocessed; then, the initial liver slice is selected, and the liver is initially segmented using the level set method incorporating the gray offset field; then, the edge of the initial liver slice is optimized; finally, the initial liver slice is Slices are used as the starting point, and the liver segmentation results of adjacent slices are incorporated into the level set energy function as position constraints, and all slices are iteratively segmented up and down.

[0033] Combine below figure 1 , using a preferred embodiment to describe in detail the method for automatic liver segmentation of abdominal CT sequence images based on level sets and shape descriptors of the present invention.

[0034] 1. Pretreatment. ...

Embodiment 2

[0048] The Sliver07 and XHCSU14 databases were tested using the method in Example 1. The Sliver07 database contains 20 abdominal CT sequences from different patients, the slice image size is 512×512, the plane pixel spacing is distributed in the range of 0.5762mm to 0.8125mm, and the slice thickness is distributed in the range of 0.7mm to 3.0mm; XHCSU14 database Provided by Xiangya Hospital of Central South University, the database contains 20 abdominal CT sequences from different patients. The slice image size is 512×512, the plane pixel pitch ranges from 0.5313mm to 0.7402mm, and the slice thickness is 1.0mm and 1.5mm . Volume Overlap Error (Volumetric Overlap Error, VOE), Relative Volume Difference (Relative Volume Difference, RVD), Average Symmetric Surface Distance (Average Symmetric Surface Distance, ASD), Root Mean Square Symmetric Surface Distance (Root Mean Square Symmetric Surface Distance, RMSD) and Maximum Symmetric Surface Distance (MaximumSymmetric Surface Dista...

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Abstract

The invention discloses a liver automatic segmentation method of abdominal CT sequence images based on a level set and a shape descriptor. The method includes: preprocessing an input image, and removing irrelevant organs and tissues; in combination with the gray shift field, constructing a level set energy function by using the correlation between adjacent slices of an abdominal CT sequence, and completing the automatic liver segmentation of abdominal CT sequence images by iterative strategy with the initial slices as the starting point; constructing the local and global shape descriptors to remove the over-segmented region and optimize the liver edge. The method of the invention can effectively segment liver regions in abdominal CT sequence images which are seriously polluted by noise andhave gray heterogeneity, can effectively avoid missegmentation of adjacent tissues around the liver, remove over-segmented regions caused by gray overlap, and improve liver segmentation accuracy.

Description

technical field [0001] The invention relates to the field of medical image analysis and processing, in particular to an automatic liver segmentation method for abdominal CT sequence images based on level sets and shape descriptors. Background technique [0002] my country is a country with a large number of liver diseases, and more than half of the new cases of liver diseases and deaths due to liver diseases in the world occur in China. Currently, the main treatment methods for liver diseases include liver resection, living donor liver transplantation, and stereotactic radiotherapy. Computer-aided diagnosis and surgical planning are an important part of the treatment of liver diseases. Accurate liver image segmentation is the basis of computer-aided diagnosis and surgical planning, and can provide technical support for liver lesion analysis, surgical navigation, and radiotherapy planning. Due to the large number of slices in abdominal CT sequence images, it is time-consumi...

Claims

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Application Information

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IPC IPC(8): G06T7/11
CPCG06T7/11G06T2207/30056G06T2207/10016G06T2207/10081
Inventor 赵于前李阳廖苗廖胜辉杨振
Owner 湖南提奥医疗科技有限公司
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