scholarly journals Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

2022 ◽  
Author(s):  
Enrica Cavedo ◽  
Philippe Tran ◽  
Urielle Thoprakarn ◽  
Jean-Baptiste Martini ◽  
Antoine Movschin ◽  
...  
Keyword(s):  
Clinical Trials ◽  
White Matter ◽  
Neurological Diseases ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Relative Volume ◽  
White Matter Hyperintensity ◽  
Dice Similarity Coefficient ◽  
Analysis Tool ◽  
Clinical Routine

Abstract Objectives QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. Methods Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland–Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. Results The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. Conclusions QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. Key Points • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Segmentation of Intracerebral Hemorrhage based on Improved U-Net

2021 ◽  
Author(s):  
Cao Guogang ◽  
Wang Yijie ◽  
Zhu Xinyu ◽  
Li Mengxue ◽  
Wang Xiaoyan ◽  
...  
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Intracerebral Hemorrhage ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Ct ◽  
Fcm Clustering ◽  
Better Than

Automatic medical image segmentation effectively aids in stroke diagnosis and treatment. In this article, an improved U-Net neural network for auxiliary diagnosis of intracerebral hemorrhage is proposed, which can realize the automatic segmentation of hemorrhage from brain CT images. The pixels of brain CT images are first clustered into four classes: gray matter, white matter, cerebrospinal fluid, and hemorrhage by fuzzy c-means (FCM) clustering, followed by the removal of the skull by morphological imaging, and finally an improved U-Net neural network model is proposed to automatically segment hemorrhages from the brain CT images. Experiment results showed that the objective function of binary cross-entropy was better than dice loss and focal loss for the proposed method. Its dice similarity coefficient reached 0.860 ± 0.031, which was better than the methods of white matter FCM clustering and multipath context generation adversarial networking. This improved method dramatically enhanced the accuracy of segmentation for intracerebral hemorrhage.

Reproducibility of volume and asymmetry measurements of hippocampus, amygdala, and entorhinal cortex on traveling volunteers: a multisite MP2RAGE prospective study

2020 ◽  
pp. 028418512096391
Author(s):  
Jiachen Du ◽  
Peipeng Liang ◽  
Hongjian He ◽  
Qiqi Tong ◽  
Ting Gong ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Healthy Aging ◽  
Structural Parameters ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Dice Similarity Coefficient ◽  
Automated Method ◽  
Segmentation Methods ◽  
Intraclass Correlations ◽  
The Impact

Background Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures—including the hippocampus, amygdala, and entorhinal cortex—in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases. Purpose To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset. Material and Methods Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data. Results ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349–0.529, DSC = 0.380–0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods. Conclusion This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies.

scholarly journals Spatial signature of white matter hyperintensities in stroke patients

2018 ◽  
Author(s):  
Markus D. Schirmer ◽  
Anne-Katrin Giese ◽  
Panagiotis Fotiadis ◽  
Mark R. Etherton ◽  
Lisa Cloonan ◽  
...  
Keyword(s):  
White Matter ◽  
Cerebral Artery ◽  
Neurological Diseases ◽  
Relative Increase ◽  
White Matter Hyperintensity ◽  
Vascular Territory ◽  
Stroke Patients ◽  
Stroke Subtype ◽  
Mri Scans ◽  
Spatial Specificity

AbstractPurposeWhite matter hyperintensity (WMH) is a common phenotype across a variety of neurological diseases, particularly prevalent in stroke patients; however, vascular territory dependent variation in WMH burden has not yet been identified. Here, we sought to investigate the spatial specificity of WMH burden in patients with acute ischemic stroke (AIS).Materials and MethodsWe created a novel age-appropriate high-resolution brain template and anatomically delineated the cerebral vascular territories. We used WMH masks derived from the clinical T2 FLAIR MRI scans and spatial normalization of the template to discriminate between WMH volume within each subject’s anterior cerebral artery (ACA), middle cerebral artery (MCA), and posterior cerebral artery (PCA) territories. Linear regression modeling including age, sex, common vascular risk factors, and TOAST stroke subtypes was used to assess for spatial specificity of WMH volume (WMHv) in a cohort of 882 AIS patients.ResultsMean age of this cohort was 65.23±14.79 years, 61.7% were male, 63.6% were hypertensive, 35.8% never smoked. Mean WMHv was 11.58c +/-13.49 cc. There were significant differences in territory-specific, relative to global, WMH burden. In contrast to PCA territory, age (0.018±0.002, p<0.001) and small-vessel stroke subtype (0.212±0.098, p<0.001) were associated with relative increase of WMH burden within the anterior (ACA and MCA) territories, whereas male sex (−0.275±0.067, p<0.001) was associated with a relative decrease in WMHv.ConclusionsOur data establish the spatial specificity of WMH distribution in relation to vascular territory and risk factor exposure in AIS patients and offer new insights into the underlying pathology.

scholarly journals Validation of an automatic tool for the measurement of brain atrophy and white matter hyperintensity in clinical routine: QyScore ®

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Enrica Cavedo ◽  
Philippe Tran ◽  
Urielle Thoprakarn ◽  
Jean‐Baptiste Martini ◽  
Antoine Movschin ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Atrophy ◽  
White Matter Hyperintensity ◽  
Clinical Routine ◽  
Automatic Tool

scholarly journals Whole brain and deep gray matter structure segmentation: Quantitative comparison between MPRAGE and MP2RAGE sequences

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254597
Author(s):  
Amgad Droby ◽  
Avner Thaler ◽  
Nir Giladi ◽  
R. Matthew Hutchison ◽  
Anat Mirelman ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Neurological Diseases ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Imaging Biomarkers ◽  
Precentral Gyrus ◽  
Tissue Segmentation ◽  
Whole Brain ◽  
Volumetric Assessment ◽  
Total Brain

Objective T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Methods Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Results Significantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; p<0.0001). Significantly higher total brain GM, and lower cerebrospinal fluid‏ volumes were obtained from MP2RAGE vs. MP-RAGE based on all segmentation methods (p<0.05 in all cases). Whole-brain voxel-wise comparisons revealed higher GM tissue probability in the thalamus, putamen, caudate, lingual gyrus, and precentral gyrus based on MP2RAGE compared with MP-RAGE. Moreover, significantly higher WM probability was observed in the cerebellum, corpus callosum, and frontal-and-temporal regions in MP2RAGE vs. MP-RAGE. Finally, MP2RAGE showed a higher mean percentage of change in total brain GM compared to MP-RAGE. On the other hand, MP-RAGE demonstrated a higher overtime percentage of change in WM and DGM volumes compared to MP2RAGE. Conclusions Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

scholarly journals Automatic segmentation of dentate nuclei for microstructure assessment: example of application to temporal lobe epilepsy patients

2020 ◽  
Author(s):  
Marta Gaviraghi ◽  
Giovanni Savini ◽  
Gloria Castellazzi ◽  
Fulvia Palesi ◽  
Nicolò Rolandi ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Gold Standard ◽  
Neurological Diseases ◽  
Automatic Segmentation ◽  
Quantitative Mri ◽  
Dice Similarity Coefficient ◽  
Human Connectome Project ◽  
Probability Threshold ◽  
Automatic Methods

AbstractDentate nuclei (DNs) segmentation is helpful for assessing their potential involvement in neurological diseases. Once DNs have been segmented, it becomes possible to investigate whether DNs they are microstructurally affected, through analysis of quantitative MRI parameters, such as the ones derived from diffusion weighted imaging (DWI). This study, therefore, aimed to develop a fully automated segmentation method using the non-DWI (b0) images from a DWI dataset to obtain DN masks inherently registered with parameter maps.Three different automatic methods were applied to healthy subjects in order to segment the DNs: registration to SUIT (a spatially unbiased atlas template of the cerebellum and brainstem), OPAL (Optimized Patch Match for Label fusion) and CNN (Convolutional Neural Network). DNs manual segmentation was considered the gold standard. Results show that the segmentation obtained with SUIT has an average Dice Similarity Coefficient (DSC) of 0.4907±0.0793 between the automatic SUIT masks and the gold standard. A comparison with manual masks was also performed for OPAL (DSC = 0.7624 ± 0.1786) and CNN (DSC = 0.8658 ± 0.0255), showing a better performance when using CNN.OPAL and CNN were optimised on heathy subjects’ data with high spatial resolution from the Human Connectome Project. The three methods were further used to segment the DNs of a subset of subjects affected by Temporal Lobe Epilepsy (TLE). This subset was derived from a 3T MRI research study which included DWI data acquired with a coarser resolution. In TLE dataset, SUIT performed similarly to using the HCP dataset, with a DSC = 0.4145 ± 0.1023. Using TLE data, OPAL performed worse than using HCP data: after changing the probability threshold the DSC was 0.4522 ± 0.1178.CNN was able to extract the DNs using the TLE data without need for retraining and with a good DSC = 0.7368 ± 0.0799. Statistical comparison of quantitative parameters derived from DWI analysis, as well as volumes of each DN, revealed altered and lateralised changes in TLE patients compared to healthy controls.The proposed CNN is therefore a viable option for accurate extraction of DNs from b0 images of DWI data with different resolutions and acquired at different sites.

scholarly journals Assessment of Artificial Intelligence Automatic Multiple Sclerosis Lesion Delineation Tool for Clinical Use

2021 ◽  
Author(s):  
Amalie Monberg Hindsholm ◽  
Stig Præstekjær Cramer ◽  
Helle Juhl Simonsen ◽  
Jette Lautrup Frederiksen ◽  
Flemming Andersen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Quantitative Evaluation ◽  
White Matter Lesions ◽  
Qualitative Evaluation ◽  
White Matter Hyperintensity ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Accuracy Score ◽  
Clinical Value

Abstract Purpose To implement and validate an existing algorithm for automatic delineation of white matter lesions on magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) on a local single-center dataset. Methods We implemented a white matter hyperintensity segmentation model, based on a 2D convolutional neural network, using the conventional T2-weighted fluid attenuated inversion recovery (FLAIR) MRI sequence as input. The model was adapted for delineation of MS lesions by further training on a local dataset of 93 MS patients with a total of 3040 lesions. A quantitative evaluation was performed on ten test patients, in which model-generated masks were compared to manually delineated masks from two expert delineators. A subsequent qualitative evaluation of the implemented model was performed by two expert delineators, in which generated delineation masks on a clinical dataset of 53 patients were rated acceptable (< 10% errors) or unacceptable (> 10% errors) based on the total number of true lesions. Results The quantitative evaluation resulted in an average accuracy score (F1) of 0.71, recall of 0.77 and dice similarity coefficient of 0.62. Our implemented model obtained the highest scores in all three metrics, when compared to three out of the box lesion segmentation models. In the clinical evaluation an average of 94% of our 53 model-generated masks were rated acceptable. Conclusion After adaptation to our local dataset, the implemented segmentation model was able to delineate MS lesions with a high clinical value as rated by delineation experts while outperforming popular out of the box applications. This serves as a promising step towards implementation of automatic lesion delineation in our MS clinic.

scholarly journals Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

2021 ◽  
Author(s):  
Parisa Mojiri Forooshani ◽  
Mahdi Biparva ◽  
Emmanuel E. Ntiri ◽  
Joel Ramirez ◽  
Lyndon Boone ◽  
...  
Keyword(s):  
White Matter ◽  
Signal To Noise Ratio ◽  
Parameter Tuning ◽  
Vascular Lesions ◽  
White Matter Hyperintensity ◽  
Dice Similarity Coefficient ◽  
Increased Risk ◽  
Uncertainty Estimates ◽  
Mri Sequences ◽  
Modified Hausdorff Distance

White matter hyperintensities (WMH) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D Convolutional Neural Network (CNN) with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. 432 subjects were recruited to train the CNNs from four multi-site imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multi-site study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on ′clinical adversarial cases′ simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io

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