Dataset Information

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

ABSTRACT:

Objectives

We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples.

Materials and methods

T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov-Smirnov test.

Results

The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85-0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83-0.94) that exceeded intra-rater similarity (Dice 0.75-0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used.

Conclusion

Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.

SUBMITTER: Damangir S

PROVIDER: S-EPMC5440501 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

Damangir Soheil S Westman Eric E Simmons Andrew A Vrenken Hugo H Wahlund Lars-Olof LO Spulber Gabriela G

Magma (New York, N.Y.) 20161209 3

<h4>Objectives</h4>We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples.<h4>Materials and methods</h4>T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) we ...[more]

PMID: 27943055

Similar Datasets

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.

Dataset Information

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

Objectives

Materials and methods

Results

Conclusion

Publications

Reproducible segmentation of white matter hyperintensities using a new statistical definition.

Similar Datasets

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