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An automatic extraction, first-time technology, applied in the field of image processing, can solve the problems of not being fully automated, not applicable to longitudinal data set processing, user-friendliness, and comprehensive software package availability, etc. The effect of chemical properties and accurate extraction results
Active Publication Date: 2018-06-15
北京天智讯泽科技有限责任公司
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However, most of these tools are designed for specific research rather than publicly available as user-friendly and comprehensive software packages
However, most available WMH segmentation toolboxes have not been evaluated in different samples with different scanners and parameters, or generally do not provide comprehensive information on WMH in subregions
Furthermore, they are usually not fully automated and require manual tracking of several brains in the study cohort for training; most available WMH segmentation toolboxes are primarily intended for horizontal studies and cannot be adapted for processing on longitudinal datasets
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Embodiment 1
[0063] For longitudinal data sets, that is, image data sets composed of FLAIR images collected at different time points for the same participant, the workflow of each module is as follows:
[0064] 1. Organization Type Segmentation Module
[0065] This module is to preprocess T1 images and FLAIR images: register FLAIR images, perform tissue segmentation on T1 images, and prepare for mapping T1 images and FLAIR images to DARTEL space.
[0066] The Tissue Type Segmentation module consists of the following units:
[0067] 1) Registration unit: Since T1 images usually have higher resolution than FLAIR images and have better contrast information, they can be used to generate the DARTEL space. Therefore, for the longitudinal dataset, rigid-body registration and resampling were performed on T1 images at all other time points and FLAIR images at all time points as source images, using the T1 image at the first time point as a reference.
[0068] 2) T1 image segmentation unit: Tissue...
Embodiment 2
[0104] For the horizontal data set, that is, for all participants, the image data set composed of FLAIR images collected at the same time point, the workflow of each module is as follows:
[0105] 1. Organization Type Segmentation Module
[0106] This module is to preprocess T1 images and FLAIR images: register FLAIR images, perform tissue segmentation on T1 images, and prepare for mapping T1 images and FLAIR images to DARTEL space.
[0107] The Tissue Type Segmentation module consists of the following units:
[0108] 1) Registration unit: Since T1 images usually have higher resolution than FLAIR images and have better contrast information, they can be used to generate the DARTEL space. Therefore, for the transverse data set, firstly, the T1 image is used as a reference, and the corresponding FLAIR image is used as the source image to perform rigid body registration, and then the rigid body registered FLAIR image is resampled to obtain the registered and resampled FLAIR image...
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Abstract
The invention discloses a cluster-based automatic WMH extraction system. The system comprises a tissue type segmentation module, a DARTEL standard segmentation module, a non-brain tissue removal module, a WMH segmentation module and a WMH refined division module, wherein the tissue type segmentation module is used for preprocessing a plurality of RLAIR images and a plurality of T1 images of a plurality of participants; the DARTEL standard segmentation module is sued for mapping the images obtained by the tissue type segmentation module into a DARTEL space; the non-brain tissue removal module is used for removing non-brain tissues of the FLARI images and the T1 images in the DARTEL space, and respectively marking the obtained images as D-FLAIR images and D-T1 images; the WMH segmentation module is sued for segmenting the D-FLAIR images and the D-T1 images on the basis of the DARTEL space so as to obtain a WMH map; and the WMH refined division module is used for carrying out refined division on the WMH map so as to obtain an intracerebroventricular white matter hyperintensities area PVWMH and a deep white matter hyperintensities area DWMH. The system is an automatic WMH extraction system which is capable of segmenting white matter focuses on the basis of longitudinal data sets and carrying out refined division on the WMH and has favorable generalization performance.
Description
technical field [0001] The invention belongs to the field of image processing, in particular to a cluster-based white matter hyperintensity (WMH) automatic extraction system. Background technique [0002] White matter hyperintensities (WMH), also known as unknown bright objects (UBOs), are abnormally hyperintense regions in the white matter observed on T2-weighted magnetic resonance imaging (MRI) scans, such as fluid-attenuated inversion recovery ( FLAIR) sequence. Ischemia-induced demyelination and axonal loss are considered as underlying mechanisms. Many factors, including vascular and genetic components, contribute to the formation and progression of WMH. It is present in about 50% of adults in their 40s and the proportion of WHM increases with age. [0003] As a biomarker of cerebral ischemia, WHM is closely related to various pathological processes, including stroke and dementia. WMH accumulation was significantly higher in stroke patients than in healthy controls, ...
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