Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia

Background: White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods: We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Results: Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion: To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia

DataSheet1_Automated Morphological Analysis of Microglia After Stroke.pdf

<p>Microglia are the resident immune cells of the brain and react quickly to changes in their environment with transcriptional regulation and morphological changes. Brain tissue injury such as ischemic stroke induces a local inflammatory response encompassing microglial activation. The change in activation status of a microglia is reflected in its gradual morphological transformation from a highly ramified into a less ramified or amoeboid cell shape. For this reason, the morphological changes of microglia are widely utilized to quantify microglial activation and studying their involvement in virtually all brain diseases. However, the currently available methods, which are mainly based on manual rating of immunofluorescent microscopic images, are often inaccurate, rater biased, and highly time consuming. To address these issues, we created a fully automated image analysis tool, which enables the analysis of microglia morphology from a confocal Z-stack and providing up to 59 morphological features. We developed the algorithm on an exploratory dataset of microglial cells from a stroke mouse model and validated the findings on an independent data set. In both datasets, we could demonstrate the ability of the algorithm to sensitively discriminate between the microglia morphology in the peri-infarct and the contralateral, unaffected cortex. Dimensionality reduction by principal component analysis allowed to generate a highly sensitive compound score for microglial shape analysis. Finally, we tested for concordance of results between the novel automated analysis tool and the conventional manual analysis and found a high degree of correlation. In conclusion, our novel method for the fully automatized analysis of microglia morphology shows excellent accuracy and time efficacy compared to traditional analysis methods. This tool, which we make openly available, could find application to study microglia morphology using fluorescence imaging in a wide range of brain disease models.</p

Video2_Automated Morphological Analysis of Microglia After Stroke.MP4

<p>Microglia are the resident immune cells of the brain and react quickly to changes in their environment with transcriptional regulation and morphological changes. Brain tissue injury such as ischemic stroke induces a local inflammatory response encompassing microglial activation. The change in activation status of a microglia is reflected in its gradual morphological transformation from a highly ramified into a less ramified or amoeboid cell shape. For this reason, the morphological changes of microglia are widely utilized to quantify microglial activation and studying their involvement in virtually all brain diseases. However, the currently available methods, which are mainly based on manual rating of immunofluorescent microscopic images, are often inaccurate, rater biased, and highly time consuming. To address these issues, we created a fully automated image analysis tool, which enables the analysis of microglia morphology from a confocal Z-stack and providing up to 59 morphological features. We developed the algorithm on an exploratory dataset of microglial cells from a stroke mouse model and validated the findings on an independent data set. In both datasets, we could demonstrate the ability of the algorithm to sensitively discriminate between the microglia morphology in the peri-infarct and the contralateral, unaffected cortex. Dimensionality reduction by principal component analysis allowed to generate a highly sensitive compound score for microglial shape analysis. Finally, we tested for concordance of results between the novel automated analysis tool and the conventional manual analysis and found a high degree of correlation. In conclusion, our novel method for the fully automatized analysis of microglia morphology shows excellent accuracy and time efficacy compared to traditional analysis methods. This tool, which we make openly available, could find application to study microglia morphology using fluorescence imaging in a wide range of brain disease models.</p

Linear regression analysis of mean value of ADC with and without CSF removal, showing the effects of different scanner versions.

<p>Linear regression analysis of mean value of ADC with and without CSF removal, showing the effects of different scanner versions.</p

Bland–Altman plots for the mean value of ADC vs MD histograms after CSF removal on GE scanners with upgrade Signa 11 new and Signa 12+.

<p>Bland–Altman plots for the mean value of ADC vs MD histograms after CSF removal on GE scanners with upgrade Signa 11 new and Signa 12+.</p

Effects of clinical scores, age and sex (represented by their regression coefficients) compared to the random scanner effect (represented by its standard deviation) on different ADC histogram parameters.

<p>Note that no test was performed for random effects.</p

Intra-class correlation coefficient between ADC and MD histogram parameters (n denotes the number of scans).

<p>Values of 0.833* and of 0.719** after removal of two outliers.</p

Bland–Altman plots for parameters derived from ADC histograms with and without application of different image corrections.

<p>Bland–Altman plots for parameters derived from ADC histograms with and without application of different image corrections.</p

Cortical regions with a significant increase in 7 Tesla T2* in CADASIL patients compared to age and sex matched healthy subjects from the GLM analysis.

<p>Cortical thickness measurements in these regions of interest (ROI) are also reported.</p>1<p>max uncorrected p-value in the cluster, p_right >0.003, p_left >0.001 (False discovery rate = 0.05);</p>2<p>cluster area >50 mm²;</p>3<p>adjusted for age, gender and level of education (p-value obtained at the cluster level).</p><p>Cortical regions with a significant increase in 7 Tesla T2* in CADASIL patients compared to age and sex matched healthy subjects from the GLM analysis.</p

Correlation coefficients between ADC and MD histogram parameters (n denotes the number of scans); all p-values are less than 0.0001

<p>Values of 0.935* and 0.779** after removal of two outliers.</p