Automatic Segmentation and Quantitative Analysis of White Matter Hyperintensities on FLAIR Images Using Trimmed-Likelihood Estimator

Rationale and Objectives

Quantitative analysis of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images provides information for disease tracking, therapeutic efficacy assessment, and cognitive science research. This study developed an automatic segmentation method to detect and quantify WMHs on FLAIR images. This study aims to assess the accuracy and reproducibility of the proposed method.

Materials and Methods

The FLAIR images of 82 patients (58–84 years) with different WMH burdens were acquired with the same 3T scanner (Intera-achieva SMI-2.1; Philip Medical System, Sixth Affiliated People’s Hospital, Shanghai, China). The FLAIR images were preprocessed through brain extraction and intensity inhomogeneity correction. An anatomy atlas built from a set of 20 patients with different WMH burdens (mild, 11 patients; moderate, 6 patients; and severe, 3 patients) was used to estimate a control parameter in the framework of segmentation. The general flow for WMH segmentation included classification of foreground and background regions, detection of abnormally high signals, and WMH refinement. The performance of automatic segmentation was evaluated by a volumetric comparison with manual segmentation on patients with different WMH burdens.

Results

Similarity index values for the accuracy of automatic segmentation compared to manual segmentation were consistently high for patients with different WMH burdens (mild, 0.78 ± 0.08; moderate, 0.83 ± 0.06; severe, 0.84 ± 0.08; and total, 0.80 ± 0.08). Linear regression demonstrated a strong correlation between the WMH volumes measured by the two methods in all patients ( r = 0.98, P = .006). Small average differences were detected between the WMH volumes obtained through manual and automatic segmentations (mild, 4.76%; moderate, 6.84%; and severe, 7.59%). The results of Bland–Altman analysis show a system bias of 0.68 cm 3 and a standard deviation of 2.10 cm 3 over the range of 2.58–53.9 cm 3 .

Conclusions

The developed method is accurate and efficient in detecting and quantifying differently sized WMHs on FLAIR images. Automatic segmentation is a promising computer-aided diagnostic tool to study WMHs in aging and dementia in basic research and even in clinical trials.

White matter hyperintensities (WMHs) are focal or diffuse lesions of high signals commonly found in the cerebral white matter (WM) on T 2 -weighted (T 2 -w) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images . The pathologic mechanism of WMHs remains unclear, but the lesions are suggested to be associated with age, demyelination, gliosis, and stroke . The typical clinical manifestations of WMHs include cognitive dysfunctions, movement disorder, and depressive symptoms . Both T 2 -w sequences and FLAIR are used to detect WMHs. FLAIR sequence exhibits a better effect than T 2 -w images when imaging WMHs near cerebrospinal fluid (CSF) spaces because it suppresses high CSF signals by adopting a long inversion time . Moreover, the contrast between WM and gray matter (GM) is reduced on FLAIR images for the elderly population, thereby producing a homogeneous low background signal and making WMHs prominent .

Accurate detection of WMHs contributes to measuring the number and volume of lesions for disease tracking, therapeutic efficacy assessment, and cognitive science research. WMHs correlate with an increased risk of stroke, dementia, and death . The issue of whether different WMH volumes are associated with cognitive dysfunctions and movement disorder has been discussed widely and constantly . Qualitative and quantitative analyses of MR images have been used to assess the lesion load of these signal abnormalities. Qualitative analysis is performed by an experienced radiologist using different visual rating scales, but the results are often affected by subjective factors and ceiling effects . Quantitative analysis methods, including manual and automatic segmentation, provide information on WMH volume . Although manual segmentation is the gold standard for validating other segmentation methods, this technique is labor intensive and time consuming. Automatic segmentation is based on machine learning and pattern recognition technique; it combines various feature selections and classification methods to detect WMHs accurately and effectively. Fuzzy connectedness and threshold-based technique are commonly used in different automatic segmentation methods. Automatic segmentation is completely reproducible, whereas manual segmentation often suffers from intra-and inter-expert variability .

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Materials and methods

Subjects and Image Acquisition

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Data Preparation

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Image Preprocessing

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Segmentation Method

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Classification of Foreground and Background Regions

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φ(yi;xi)=∑kj=1πjψ(yi;xi,μj,σj) φ

(

;

)

∑

(

;

)

where ψ ( y__i ; x__i , μ j , σ j ) denotes the PDF of a Gaussian distribution with unknown parameters, including the mean μ j , standard deviation σ j , and proportion of the j th classifier π j . Each Gaussian in GMM provides a probabilistic model for a specific tissue class in the FLAIR images. x__j is a discrete label that represents the classification of voxel y__i with respect to the two tissue classes ( k = 2), namely, foreground and background. In the EM algorithm, unknown parameters must be properly initialized using the Otsu method . Specifically, we first used the Otsu method to classify the FLAIR images into two parts, namely, the foreground and background regions. μ(0)j μ

(

) and σ(0)j σ

(

) were then initialized using the mean values and standard deviations of these preclassified regions. Furthermore, we computed the proportions of the foreground and background regions with respect to the images as a whole. These proportions were used to initialize the parameters π(0)j π

(

) . With these initial starting values, the EM algorithm estimates MLE parameters by iteratively performing expectation (E) and maximization (M) steps. The former step creates an expectation function of log likelihood using the estimated unknown parameters. The latter step estimates the unknown parameters and maximizes the expectation function . The final GMM was estimated by MLE with the EM algorithm. The probabilities of voxel y__i assigned to the foreground and background were calculated based on Bayes posterior probability in the EM algorithm. The brain voxels were finally classified into the foreground and background regions. The corresponding PDF φ ( y__i ) for each voxel y__i was stored until used to detect abnormally high signals on the FLAIR images. The results obtained after the classification of the foreground (white) and background (black) regions are shown in Figure 3 b.

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Detection of Abnormally High Signals

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θˆTLE:=argmaxθ∈Θ∑nTi=1f(yv(i);θ) θ

TLE

arg

max

∈

∑

(

)

;

)

where f(yi;θ)=logφ(yi;θ) f

(

;

)

log

(

;

) is the logarithmic value of PDF for voxel y__i in a FLAIR image and f(yv(1);θ)≥f(yv(2);θ)≥⋅⋅⋅≥f(yv(nT);θ) f

(

)

;

)

≥

(

)

;

)

≥

⋅

≥

(

)

;

) . The corresponding permutation of the indices is represented as v = [ v (1), …, v ( n__T )], which sorts all voxels of the FLAIR images according to the values of their probability f ( y__v ( i ) ;θ). The number of voxels for normal tissues was calculated using the trimming parameter n__T = n × (1 − h ), where n is the denoted total number of voxels in FLAIR images and h represents the proportion of abnormal high signals to the FLAIR images. The detection of abnormally high signals on FLAIR images was divided into two stages: parameter h estimation and TLE-EM segmentation.

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Stage 1: Estimation of Parameter h

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Stage 2: TLE-EM Segmentation

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Validation of Segmentation and Quantitative Measures

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SI=2×(M∩A)M+A SI

(

∩

)

FPR=!M∩AM FPR

∩

FNR=M∩!AM FNR

∩

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Results

Volumetric Comparison Between Manual and Automatic Segmentations

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Table 1

Similarity Measurement Comparison Between Results of Automatic Segmentation and Manual Segmentation

WMH Burden SI FPR FNR Mild ( N = 44) 0.78 ± 0.08 0.26 ± 0.06 0.19 ± 0.06 Moderate ( N = 26) 0.83 ± 0.06 0.11 ± 0.06 0.21 ± 0.06 Severe ( N = 12) 0.84 ± 0.08 0.08 ± 0.06 0.22 ± 0.07 Total ( N = 82) 0.80 ± 0.08 0.19 ± 0.07 0.20 ± 0.06

FNR, false-negative rate; FPR, false-positive rate; SI, similarity index; WMH, white matter hyperintensity.

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Regression and Bland–Altman Analysis

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Table 2

Quantitative Analysis of Automated Segmentation with Respect to Manual Segmentation

WMH Burden Automatic (cm 3 ) Manual (cm 3 )R Mild ( N = 44) 6.84 ± 2.10 6.5 ± 1.96 0.91 Moderate ( N = 26) 18.83 ± 4.87 20.1 ± 4.93 0.92 Severe ( N = 12) 37.73 ± 8.51 40.7 ± 7.61 0.93 Total ( N = 82) 15.16 ± 11.69 15.84 ± 12.73 0.98

Automatic and manual refer to quantitative white matter hyperintensity (WMH) volume detected by automatic and manual segmentation methods, respectively.

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Effect of Different Parameters h Values on Automatic Segmentation of WMHs

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Discussion

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Table 3

Comparison of Similarity Index for the WMH Segmentation Between Different Methods on Different Real Data Sets

WMH Burden Mild Moderate Severe Total TLE-EM 0.78 0.83 0.84 0.80 Anbeek et al. 0.50 0.75 0.85 0.80 Behloul et al. 0.70 0.75 0.82 0.75 Khayati et al. 0.73 0.75 0.81 0.75

EM, expectation–maximization; TLE, trimmed-likelihood estimator; WMH, white matter hyperintensity.

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Conclusions

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Acknowledgments

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Automatic Segmentation and Quantitative Analysis of White Matter Hyperintensities on FLAIR Images Using Trimmed-Likelihood Estimator

Rationale and Objectives

Materials and Methods

Results

Conclusions

Materials and methods

Subjects and Image Acquisition

Data Preparation

Image Preprocessing

Segmentation Method

Classification of Foreground and Background Regions

Detection of Abnormally High Signals

Stage 1: Estimation of Parameter h

Stage 2: TLE-EM Segmentation

WMHs Refinement

Validation of Segmentation and Quantitative Measures

Results

Volumetric Comparison Between Manual and Automatic Segmentations

Regression and Bland–Altman Analysis

Effect of Different Parameters h Values on Automatic Segmentation of WMHs

Discussion

Conclusions

Acknowledgments

References

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