Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities

Dan Wu; Marilyn Albert; Anja Soldan; Corinne Pettigrew; Kenichi Oishi; Yusuke Tomogane; Chenfei Ye; Ting Ma; Michael I Miller; Susumu Mori

doi:10.1016/j.nicl.2019.101772

Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities

Neuroimage Clin. 2019:22:101772. doi: 10.1016/j.nicl.2019.101772. Epub 2019 Mar 13.

Authors

Affiliations

¹ Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China; Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: danwu.bme@zju.edu.cn.
² Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
³ Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴ Department of Biomedicine Engineering, Johns Hopkins University, Baltimore, MD, USA; Center of Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
⁵ Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.

Abstract

The extent and spatial location of white matter hyperintensities (WMH) on brain MRI may be relevant to the development of cognitive decline in older persons. Here, we introduce a new method, known as the Multi-atlas based Detection and Localization (MADL), to evaluate WMH on fluid-attenuated inversion recovery (FLAIR) data. This method simultaneously parcellates the whole brain into 143 structures and labels hyperintense areas within each WM structure. First, a multi-atlas library was established with FLAIR data of normal elderly brains; and then a multi-atlas fusion algorithm was developed by which voxels with locally abnormal intensities were detected as WMH. At the same time, brain segmentation maps were generated from the multi-atlas fusion process to determine the anatomical location of WMH. Areas identified using the MADL method agreed well with manual delineation, with an interclass correlation of 0.97 and similarity index (SI) between 0.55 and 0.72, depending on the total WMH load. Performance was compared to other state-of-the-art WMH detection methods, such as BIANCA and LST. MADL-based analyses of WMH in an older population revealed a significant association between age and WMH load in deep WM but not subcortical WM. The findings also suggested increased WMH load in selective brain regions in subjects with mild cognitive impairment compared to controls, including the inferior deep WM and occipital subcortical WM. The proposed MADL approach may facilitate location-dependent characterization of WMH in older individuals with memory impairment.

Keywords: FLAIR; Mild cognitive disorder; Multi-atlas; Segmentation; White matter hyperintensities.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Algorithms*
Atlases as Topic*
Cognitive Dysfunction / diagnostic imaging*
Humans
Image Interpretation, Computer-Assisted / methods*
Leukoaraiosis / diagnostic imaging*
Magnetic Resonance Imaging / methods*
Neuroimaging / methods*

Abstract

Publication types

MeSH terms

Grants and funding