3D automatic glioma segmentation method combining Volume of Interest and GrowCut algorithm

A region of interest and glioma technology, applied in the field of 3D automatic glioma segmentation, can solve the problems of time-consuming and user-dependent experience

Inactive Publication Date: 2015-11-11
FUDAN UNIV
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Problems solved by technology

Manually marking gliomas needs to be marked on continuous cross-sectional images, which is very time-consuming and depends on the user's experience

Method used

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  • 3D automatic glioma segmentation method combining Volume of Interest and GrowCut algorithm
  • 3D automatic glioma segmentation method combining Volume of Interest and GrowCut algorithm
  • 3D automatic glioma segmentation method combining Volume of Interest and GrowCut algorithm

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Embodiment Construction

[0049] The following are the specific implementation steps of the entire algorithm:

[0050] (1) First, read the MR-T2flair image, and perform operations such as fitting, rotating, and cutting on the brain image, so that it can be divided into two symmetrical cuboids. Reuse the extended Boundingbox method [1] , to detect the region of interest.

[0051] (2) Apply the reflective symmetry method to the region of interest obtained in step 1 which has been appropriately enlarged, detect and correct it, and obtain a more accurate VOI of the region of interest.

[0052] (3) According to the obtained VOI of the region of interest, mark the seed point, and finally determine the mark of the voxel point through the iterative process of GrowCut, that is, the glioma area is marked as "+1", and the background area is marked as "-1". ", thereby segmenting the glioma.

[0053] Result analysis, From figure 1 and Figure 4 The results show that the extended 3DBoundingbox method can acc...

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Abstract

The invention belongs to the technical field of image segmentation, and specifically relates to a 3D full-automatic glioma segmentation method combining a Volume of Interest and a GrowCut algorithm. The method first expands a Bounding box algorithm to 3D, utilizes the algorithm to extract a Volume of Interest (VOI) containing glioma, then utilizes a reflection symmetry algorithm to estimate the VOI and overcomes the defects of Bounding box when the glioma is detected to cross over a median sagittal plane, and finally marks pixel points in an image based on the accurate VOI, and thus a semi-automatic 2D GrowCut algorithm is optimized to a full-automatic 3D segmentation method. While accurately segmenting the glioma, the method is more rapid in theory and reality compared with the 2D segmentation algorithm of the same principle, and is better in convenience and feasibility compared with a manual segmentation method. As an image segmentation method, the 3D full-automatic glioma segmentation method provided by the invention can serve as a powerful auxiliary tool for clinical diagnosis of glioma.

Description

technical field [0001] The invention belongs to the technical field of image segmentation, and in particular relates to a 3D automatic brain glioma segmentation method combined with a region-of-interest GrowCut algorithm. Background technique [0002] Glioma is the most common malignant tumor of the central nervous system. Surgical resection combined with postoperative radiotherapy and chemotherapy is currently the standard treatment for glioma. Among them, surgical resection plays a decisive role in the prognosis of patients with glioma, especially the largest safe resection has become the consensus of neurosurgery in the world. Magnetic resonance (MRI) navigation can effectively guide the operation of glioma and improve the degree of resection of glioma, especially the T2-Flair sequence image, based on this part of the brain tumor segmentation, it is helpful for preoperative calculation of tumor volume, Postoperative judgment of tumor recurrence has important diagnostic ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T2200/04G06T2207/30016G06T2207/30096
Inventor 余锦华季春红史之峰陈亮汪源源毛颖
Owner FUDAN UNIV
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