Automated segmentation of multifocal basal ganglia T2*-weighted MRI hypointensities

Abstract

Multifocal basal ganglia T2*-weighted (T2*w) hypointensities, which are believed to arise mainly from vascular mineralization, were recently proposed as a novel MRI biomarker for small vessel disease and ageing. These T2*w hypointensities are typically segmented semi-automatically, which is time consuming, associated with a high intra-rater variability and low inter-rater agreement. To address these limitations, we developed a fully automated, unsupervised segmentation method for basal ganglia T2*w hypointensities. This method requires conventional, co-registered T2*w and T1-weighted (T1w) volumes, as well as region-of-interest (ROI) masks for the basal ganglia and adjacent internal capsule generated automatically from T1w MRI. The basal ganglia T2*w hypointensities were then segmented with thresholds derived with an adaptive outlier detection method from respective bivariate T2*w/T1w intensity distributions in each ROI. Artefacts were reduced by filtering connected components in the initial masks based on their standardised T2*w intensity variance. The segmentation method was validated using a custom-built phantom containing mineral deposit models, i.e. gel beads doped with 3 different contrast agents in 7 different concentrations, as well as with MRI data from 98 community-dwelling older subjects in their seventies with a wide range of basal ganglia T2*w hypointensities. The method produced basal ganglia T2*w hypointensity masks that were in substantial volumetric and spatial agreement with those generated by an experienced rater (Jaccard index = 0.62 ± 0.40). These promising results suggest that this method may have use in automatic segmentation of basal ganglia T2*w hypointensities in studies of small vessel disease and ageing.

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke

“…Regional volume determined fully automatically from binary masks generated as per Glatz et al. (Glatz et al. ) in the deep gray matter structures and brainstem, and semiautomatically by thresholding (Valdes Hernandez et al.…”

Evaluation of MRI sequences for quantitative T1 brain mapping

“…All imaging was performed at the University of Edinburgh on a 3 Tesla Siemens Prisma scanner using a custom MRI phantom [11]. The phantom (figure 1a) is filled with 1.5 g/l CuSO4 and 3.6 g/l NaCl solution and made …”

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques

“…The examples shown thus far for automated image analyses have been mainly discussed in terms of whole brain analyses, but analyses may also be directed to a specific region, when for example there is an a priori hypothesis for a particular ROI, like the basal ganglia, cerebellum or hippocampus (Glatz et al 2015;Suppa et al 2015;Wu et al 2012). In such circumstances a Bmask^is placed over the ROI, so that comparisons are focused just within the ROI, reducing some of the restrictiveness of family wise error.…”