Cluster-based automatic WMH extraction system

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, ...

AI Technical Summary

Problems solved by technology

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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