Dataset Information

Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T.

ABSTRACT:

Purpose

To determine the precision and accuracy of an automated method for segmenting white matter hyperintensities (WMH) on fast fluid-attenuated inversion-recovery (FLAIR) images in elderly brains at 3T.

Materials and methods

FLAIR images from 18 individuals (60-82 years, 9 females) with WMH burdens ranging from 1-80 cm(3) were used. The protocol included the removal of clearly hyperintense voxels; two-class fuzzy C-means clustering (FCM); and thresholding to segment probable WMH. Two false-positive minimization (FPM) methods using white matter templates were tested. Precision was assessed by adding synthetic hyperintense voxels to brain slices. Accuracy was validated by comparing automatic and manual segmentations. Whole-brain, voxel-wise metrics of similarity, under- and overestimation were used to evaluate both precision and accuracy.

Results

Precision was high, as the lowest accuracy in the synthetic datasets was 93%. Both FPM strategies successfully improved overall accuracy. Whole-brain accuracy for the FCM segmentation alone ranged from 45%-81%, which improved to 75%-85% using the FPM strategies.

Conclusion

The method was accurate across the range of WMH burden typically seen in the elderly. Accuracy levels achieved or exceeded those of other approaches using multispectral and/or more sophisticated pattern recognition methods.

SUBMITTER: Gibson E

PROVIDER: S-EPMC2905619 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Publications

Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T.

Gibson Erin E Gao Fuqiang F Black Sandra E SE Lobaugh Nancy J NJ

Journal of magnetic resonance imaging : JMRI 20100601 6

<h4>Purpose</h4>To determine the precision and accuracy of an automated method for segmenting white matter hyperintensities (WMH) on fast fluid-attenuated inversion-recovery (FLAIR) images in elderly brains at 3T.<h4>Materials and methods</h4>FLAIR images from 18 individuals (60-82 years, 9 females) with WMH burdens ranging from 1-80 cm(3) were used. The protocol included the removal of clearly hyperintense voxels; two-class fuzzy C-means clustering (FCM); and thresholding to segment probable WM ...[more]

PMID: 20512882

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Project description:BackgroundManual segmentation is currently the gold standard to assess white matter hyperintensities (WMH), but it is time consuming and subject to intra and inter-operator variability.PurposeTo compare automatic methods to segment white matter hyperintensities (WMH) in the elderly in order to assist radiologist and researchers in selecting the most relevant method for application on clinical or research data.Material and methodsWe studied a research dataset composed of 147 patients, including 97 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 database and 50 patients from ADNI 3 and a clinical routine dataset comprising 60 patients referred for cognitive impairment at the Pitié-Salpêtrière hospital (imaged using four different MRI machines). We used manual segmentation as the gold standard reference. Both manual and automatic segmentations were performed using FLAIR MRI. We compared seven freely available methods that produce segmentation mask and are usable by a radiologist without a strong knowledge of computer programming: LGA (Schmidt et al., 2012), LPA (Schmidt, 2017), BIANCA (Griffanti et al., 2016), UBO detector (Jiang et al., 2018), W2MHS (Ithapu et al., 2014), nicMSlesion (with and without retraining) (Valverde et al., 2019, 2017). The primary outcome for assessing segmentation accuracy was the Dice similarity coefficient (DSC) between the manual and the automatic segmentation software. Secondary outcomes included five other metrics.ResultsA deep learning approach, NicMSlesion, retrained on data from the research dataset ADNI, performed best on this research dataset (DSC: 0.595) and its DSC was significantly higher than that of all others. However, it ranked fifth on the clinical routine dataset and its performance severely dropped on data with artifacts. On the clinical routine dataset, the three top-ranked methods were LPA, SLS and BIANCA. Their performance did not differ significantly but was significantly higher than that of other methods.ConclusionThis work provides an objective comparison of methods for WMH segmentation. Results can be used by radiologists to select a tool.

Project description:Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.

Project description:BackgroundThe progression of FLAIR white matter hyperintensities (WMHs) on MRI heralds vascular-mediated cognitive decline. Even before FLAIR WMH progression, adjacent normal appearing white matter (NAWM) already demonstrates microstructural deterioration on diffusion tensor imaging (DTI). We hypothesized that elevated DTI free water (FW) would precede FLAIR WMH progression, implicating interstitial fluid accumulation as a key pathological step in the progression of cerebral small vessel disease.MethodsParticipants at least 3 months after an ischemic stroke or TIA with WMH on MRI underwent serial brain MRIs every 3 months over the subsequent year. For each participant, the WMHs were automatically segmented, serial MRIs were aligned, and a region of WMH penumbra tissue at risk was defined by dilating lesions at any time point and subtracting baseline lesions. Penumbra voxels were classified as either stable or progressing to WMH if they were segmented as new lesions and demonstrated increasing FLAIR intensity over time. Aligned DTI images included FW and FW-corrected fractional anisotropy (FATissue) and mean diffusivity (MDTissue). Logistic regression and area under the receiver-operator characteristic curve (AUC) were used to test whether baseline DTI predicted voxel-wise classification of stable penumbra or progression to WMH while covarying for clinical risk factors.ResultsIn the included participants (n = 26, mean age 71 ± 9 years, 31% female), we detected a median annual voxel-wise WMH growth of 2.9 ± 2.6 ml. Each baseline DTI metric was associated with lesion progression in the penumbra, but FW had the greatest AUC of 0.732 (0.730 - 0.733) for predicting voxel-wise WMH progression pooled across participants.DiscussionBaseline increased interstitial fluid, estimated as FW on DTI, predicted the progression of NAWM to WMH over the following year. These results implicate the presence of FW in the pathogenesis of cerebral small vessel disease progression.

Dataset Information

Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T.

Purpose

Materials and methods

Results

Conclusion

Publications

Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T.

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