Comparison of Automated Brain Volume Measures obtained with NeuroQuant and FreeSurfer

Evaluating brain volume segmentation accuracy and reliability of FreeSurfer and Neurophet AQUA at variations in MRI magnetic field strengths

We aimed to compare the accuracy and reliability of two segmentation tools for magnetic resonance (MR) volumetry (FreeSurfer and Neurophet AQUA) at two magnetic field strengths (1.5T and 3T). We included 101 patients for the 1.5T-3T dataset and 112 for the 3T-3T dataset from three hospitals and five open-source datasets. The mean volume difference and average volume difference percentage with the change in magnetic field strength were compared between the methods. The hippocampus volume was larger with FreeSurfer than the Neurophet AQUA. In most brain regions, the Neurophet AQUA yielded a smaller average volume difference percentage (all < 10%) than FreeSurfer (all > 10%). The Neurophet AQUA exhibited more stable connectivity and regularity of the segmented components. Regarding volume, the Neurophet AQUA had effect sizes and ICCs comparable to those of FreeSurfer across the magnetic field strengths. With FreeSurfer, the original volume difference was small, whereas the average volume difference percentage was small with the Neurophet AQUA. Image segmentation took 1 h with FreeSurfer and 5 min with the Neurophet AQUA. When choosing an automatic segmentation method, the differences in image processing time and volume variability due to changes in the magnetic field strength of these methods should be considered.

Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction

PURPOSE

The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volumetry.

METHODS

This retrospective study collected 3D T1-weighted data using 3T from 42 participants for the simulated acceleration dataset and 48 for the validation dataset. The simulated acceleration dataset consists of three sets at different simulated acceleration levels (Simul-Accel) corresponding to level 1 (65% undersampling), 2 (70%), and 3 (75%). These images were then subjected to deep learning-based reconstruction (Simul-Accel-DL). Conventional images (Conv) without acceleration and DL were set as the reference. In the validation dataset, DICOM images were collected from Conv and accelerated scan with DL-based reconstruction (Accel-DL). The image quality of Simul-Accel-DL was evaluated using quantitative error metrics. Volumetric measurements were evaluated using intraclass correlation coefficients (ICCs) and linear regression analysis in both datasets. The volumes were estimated by two software, NeuroQuant and DeepBrain.

RESULTS

Simul-Accel-DL across all acceleration levels revealed comparable or better error metrics than Simul-Accel. In the simulated acceleration dataset, ICCs between Conv and Simul-Accel-DL in all ROIs exceeded 0.90 for volumes and 0.77 for normative percentiles at all acceleration levels. In the validation dataset, ICCs for volumes > 0.96, ICCs for normative percentiles > 0.89, and R2 > 0.93 at all ROIs except pallidum demonstrated good agreement in both software.

CONCLUSION

DL-based reconstruction achieves clinical feasibility of 3D T1 brain volumetric MRI by up to 75% acceleration relative to full-sampled acquisition.

Comparison of Inter-Method Agreement and Reliability for Automatic Brain Volumetry Using Three Different Clinically Available Software Packages

Background and Objectives: No comparative study has evaluated the inter-method agreement and reliability between Heuron AD and other clinically available brain volumetric software packages. Hence, we aimed to investigate the inter-method agreement and reliability of three clinically available brain volumetric software packages: FreeSurfer (FS), NeuroQuant® (NQ), and Heuron AD (HAD). Materials and Methods: In this study, we retrospectively included 78 patients who underwent conventional three-dimensional (3D) T1-weighed imaging (T1WI) to evaluate their memory impairment, including 21 with normal objective cognitive function, 24 with mild cognitive impairment, and 33 with Alzheimer's disease (AD). All 3D T1WI scans were analyzed using three different volumetric software packages. Repeated-measures analysis of variance, intraclass correlation coefficient, effect size measurements, and Bland-Altman analysis were used to evaluate the inter-method agreement and reliability. Results: The measured volumes demonstrated substantial to almost perfect agreement for most brain regions bilaterally, except for the bilateral globi pallidi. However, the volumes measured using the three software packages showed significant mean differences for most brain regions, with consistent systematic biases and wide limits of agreement in the Bland-Altman analyses. The pallidum showed the largest effect size in the comparisons between NQ and FS (5.20-6.93) and between NQ and HAD (2.01-6.17), while the cortical gray matter showed the largest effect size in the comparisons between FS and HAD (0.79-1.91). These differences and variations between the software packages were also observed in the subset analyses of 45 patients without AD and 33 patients with AD. Conclusions: Despite their favorable reliability, the software-based brain volume measurements showed significant differences and systematic biases in most regions. Thus, these volumetric measurements should be interpreted based on the type of volumetric software used, particularly for smaller structures. Moreover, users should consider the replaceability-related limitations when using these packages in real-world practice.

Fast Quantitative Magnetic Resonance Imaging Evaluation of Hydrocephalus Using 3-Dimensional Fluid-Attenuated Inversion Recovery: Initial Experience

OBJECTIVE

This study aimed to demonstrate the initial experience of using fast quantitative magnetic resonance imaging (MRI) to evaluate hydrocephalus.

METHODS

A total of 109 brain MRI volumetry examinations (acquisition time, 7 minutes 30 seconds) were performed in 72 patients with hydrocephalus. From the measured ventricular system and brain volumes, ventricle-brain volume percentage was calculated to standardize hydrocephalus severity (processing time, <5 minutes). The obtained values were categorized into no, mild, and severe based on the fronto-occipital horn ratio (FOHR) and the ventricle-brain volume percentages reported in the literature. The measured volumes and percentages were compared between patients with mild hydrocephalus and those with severe hydrocephalus. The diagnostic performance of brain hydrocephalus MRI volumetry was evaluated using receiver operating characteristic curve analysis.

RESULTS

Ventricular volumes and ventricle-brain volume percentages were significantly higher in in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 352.6 ± 165.6 cm 3 vs 149.1 ± 78.5 cm 3 , P < 0.001, and 26.8% [20.8%-33.1%] vs 12.1% ± 6.0%, P < 0.001; percentage-based severity: 359.5 ± 143.3 cm 3 vs 137.0 ± 62.9 cm 3 , P < 0.001, and 26.8% [21.8%-33.1%] vs 11.3% ± 4.2%, P < 0.001, respectively), whereas brain volumes were significantly lower in patients with severe hydrocephalus than in those with mild hydrocephalus (FOHR-based severity: 878.1 ± 363.5 cm 3 vs 1130.1 cm 3 [912.1-1244.2 cm 3 ], P = 0.006; percentage-based severity: 896.2 ± 324.6 cm 3 vs 1142.3 cm 3 [944.2-1246.6 cm 3 ], P = 0.005, respectively). The ventricle-brain volume percentage was a good diagnostic parameter for evaluating the degree of hydrocephalus (area under the curve, 0.855; 95% confidence interval, 0.719-0.990; P < 0.001).

CONCLUSIONS

Brain MRI volumetry can be used to evaluate hydrocephalus severity and may provide guide interpretation because of its rapid acquisition and postprocessing times.

Reliability of automated brain volumetric analysis: A test by comparing NeuroQuant and volBrain software

BACKGROUND AND PURPOSE

Brain volume analysis from magnetic resonance imaging (MRI) is gaining an important role in neurological diagnosis. This study compares the volumes of brain segments measured by two automated brain analysis software, NeuroQuant (NQ), and volBrain (VB) in order to test their reliability in brain volumetry.

METHODS

Using NQ and VB software, the same brain segment volumes were calculated and compared, taken from 56 patients scanned under the same MRI sequence. These segments were intracranial cavity, putamen, thalamus, amygdala, whole brain, cerebellum, white matter, and hippocampus. The paired t-test method has been used to determine if there was a significant difference in these measurements. The interclass correlation (ICC) is used to test inter-method reliability between the two software. Finally, regression analysis was used to examine the possibility of linear correlation.

RESULTS

In all brain segments tested but hippocampus, significant differences were found. ICC presents satisfactory to excellent reliability in all brain segments except thalamus and amygdala for which reliability has been proven to be poor. In most cases, linear correlation was found.

CONCLUSIONS

The significant differences found in the majority of the tested brain segments are raising questions about the reliability of automated brain analysis as a quantitative tool. Strong linear correlation of the volumetric measurements and good reliability indicates that, each software provides good qualitative information of brain structures size.

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Background and purpose

To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.

Materials and methods

This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.

Results

The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.

Conclusion

Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuant® vs. DeepBrain® in the Korean Population: Correlation with Cranial Shape

Purpose

This study aimed to compare the volume and normative percentiles of brain volumetry in the Korean population using quantitative brain volumetric MRI analysis tools NeuroQuant® (NQ) and DeepBrain® (DB), and to evaluate whether the differences in the normative percentiles of brain volumetry between the two tools is related to cranial shape.

Materials and Methods

In this retrospective study, we analyzed the brain volume reports obtained from NQ and DB in 163 participants without gross structural brain abnormalities. We measured three-dimensional diameters to evaluate the cranial shape on T1-weighted images. Statistical analyses were performed using intra-class correlation coefficients and linear correlations.

Results

The mean normative percentiles of the thalamus (90.8 vs. 63.3 percentile), putamen (90.0 vs. 60.0 percentile), and parietal lobe (80.1 vs. 74.1 percentile) were larger in the NQ group than in the DB group, whereas that of the occipital lobe (18.4 vs. 68.5 percentile) was smaller in the NQ group than in the DB group. We found a significant correlation between the mean normative percentiles obtained from the NQ and cranial shape: the mean normative percentile of the occipital lobe increased with the anteroposterior diameter and decreased with the craniocaudal diameter.

Conclusion

The mean normative percentiles obtained from NQ and DB differed significantly for many brain regions, and these differences may be related to cranial shape.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based nomogram

OBJECTIVES

To develop and validate a nomogram based on MRI features for predicting iNPH.

METHODS

Patients aged  ≥ 60 years (clinically diagnosed with iNPH, Parkinson's disease, or Alzheimer's disease or healthy controls) who underwent MRI including three-dimensional T1-weighted volumetric MRI were retrospectively identified from two tertiary referral hospitals (one hospital for derivation set and the other for validation set). Clinical and imaging features for iNPH were assessed. Deep learning-based brain segmentation software was used for 3D volumetry. A prediction model was developed using logistic regression and transformed into a nomogram. The performance of the nomogram was assessed with respect to discrimination and calibration abilities. The nomogram was internally and externally validated.

RESULTS

A total of 452 patients (mean age ± SD, 73.2 ± 6.5 years; 200 men) were evaluated as the derivation set. One hundred eleven and 341 patients were categorized into the iNPH and non-iNPH groups, respectively. In multivariable analysis, high-convexity tightness (odds ratio [OR], 35.1; 95% CI: 4.5, 275.5), callosal angle  < 90° (OR, 12.5; 95% CI: 3.1, 50.0), and normalized lateral ventricle volume (OR, 4.2; 95% CI: 2.7, 6.7) were associated with iNPH. The nomogram combining these three variables showed an area under the curve of 0.995 (95% CI: 0.991, 0.999) in the study sample, 0.994 (95% CI: 0.990, 0.998) in the internal validation sample, and 0.969 (95% CI: 0.940, 0.997) in the external validation sample.

CONCLUSION

A brain morphometry-based nomogram including high-convexity tightness, callosal angle  < 90°, and normalized lateral ventricle volume can help accurately estimate the probability of iNPH.

KEY POINTS

• The nomogram with MRI findings (high-convexity tightness, callosal angle, and normalized lateral ventricle volume) helped in predicting the probability of idiopathic normal-pressure hydrocephalus. • The nomogram may facilitate the prediction of idiopathic normal-pressure hydrocephalus and consequently avoid unnecessary invasive procedures such as the cerebrospinal fluid tap test, drainage test, and cerebrospinal fluid shunt surgery.

Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging

BACKGROUND AND PURPOSE

Automatic brain parcellation is typically performed on dedicated MR imaging sequences, which require valuable examination time. In this study, a 3D MR imaging quantification sequence to retrieve R1 and R2 relaxation rates and proton density maps was used to synthesize a T1-weighted image stack for brain volume measurement, thereby combining image data for multiple purposes. The repeatability and reproducibility of using the conventional and synthetic input data were evaluated.

MATERIALS AND METHODS

Twelve subjects with a mean age of 54 years were scanned twice at 1.5T and 3T with 3D-QALAS and a conventionally acquired T1-weighted sequence. Using SyMRI, we converted the R1, R2, and proton density maps into synthetic T1-weighted images. Both the conventional T1-weighted and the synthetic 3D-T1-weighted inversion recovery images were processed for brain parcellation by NeuroQuant. Bland-Altman statistics were used to correlate the volumes of 12 brain structures. The coefficient of variation was used to evaluate the repeatability.

RESULTS

A high correlation with medians of 0.97 for 1.5T and 0.92 for 3T was found. A high repeatability was shown with a median coefficient of variation of 1.2% for both T1-weighted and synthetic 3D-T1-weighted inversion recovery at 1.5T, and 1.5% for T1-weighted imaging and 4.4% for synthetic 3D-T1-weighted inversion recovery at 3T. However, significant biases were observed between the methods and field strengths.

CONCLUSIONS

It is possible to perform MR imaging quantification of R1, R2, and proton density maps to synthesize a 3D-T1-weighted image stack, which can be used for automatic brain parcellation. Synthetic parameter settings should be reinvestigated to reduce the observed bias.

Development and validation of deep learning-based automatic brain segmentation for East Asians: A comparison with Freesurfer

Purpose

To develop and validate deep learning-based automatic brain segmentation for East Asians with comparison to data for healthy controls from Freesurfer based on a ground truth.

Methods

A total of 30 healthy participants were enrolled and underwent T1-weighted magnetic resonance imaging (MRI) using a 3-tesla MRI system. Our Neuro I software was developed based on a three-dimensional convolutional neural networks (CNNs)-based, deep-learning algorithm, which was trained using data for 776 healthy Koreans with normal cognition. Dice coefficient (D) was calculated for each brain segment and compared with control data by paired t-test. The inter-method reliability was assessed by intraclass correlation coefficient (ICC) and effect size. Pearson correlation analysis was applied to assess the relationship between D values for each method and participant ages.

Results

The D values obtained from Freesurfer (ver6.0) were significantly lower than those from Neuro I. The histogram of the Freesurfer results showed remarkable differences in the distribution of D values from Neuro I. Overall, D values obtained by Freesurfer and Neuro I showed positive correlations, but the slopes and intercepts were significantly different. It was showed the largest effect sizes ranged 1.07-3.22, and ICC also showed significantly poor to moderate correlations between the two methods (0.498 ≤ ICC ≤ 0.688). For Neuro I, D values resulted in reduced residuals when fitting data to a line of best fit, and indicated consistent values corresponding to each age, even in young and older adults.

Conclusion

Freesurfer and Neuro I were not equivalent when compared to a ground truth, where Neuro I exhibited higher performance. We suggest that Neuro I is a useful alternative for the assessment of the brain volume.

3D Capsule Networks for Brain Image Segmentation

BACKGROUND AND PURPOSE

Current autosegmentation models such as UNets and nnUNets have limitations, including the inability to segment images that are not represented during training and lack of computational efficiency. 3D capsule networks have the potential to address these limitations.

MATERIALS AND METHODS

We used 3430 brain MRIs, acquired in a multi-institutional study, to train and validate our models. We compared our capsule network with standard alternatives, UNets and nnUNets, on the basis of segmentation efficacy (Dice scores), segmentation performance when the image is not well-represented in the training data, performance when the training data are limited, and computational efficiency including required memory and computational speed.

RESULTS

The capsule network segmented the third ventricle, thalamus, and hippocampus with Dice scores of 95%, 94%, and 92%, respectively, which were within 1% of the Dice scores of UNets and nnUNets. The capsule network significantly outperformed UNets in segmenting images that were not well-represented in the training data, with Dice scores 30% higher. The computational memory required for the capsule network is less than one-tenth of the memory required for UNets or nnUNets. The capsule network is also >25% faster to train compared with UNet and nnUNet.

CONCLUSIONS

We developed and validated a capsule network that is effective in segmenting brain images, can segment images that are not well-represented in the training data, and is computationally efficient compared with alternatives.

Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.

Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.

Cortical Grey matter volume depletion links to neurological sequelae in post COVID-19 "long haulers"

OBJECTIVE

COVID-19 (SARS-CoV-2) has been associated with neurological sequelae even in those patients with mild respiratory symptoms. Patients experiencing cognitive symptoms such as "brain fog" and other neurologic sequelae for 8 or more weeks define "long haulers". There is limited information regarding damage to grey matter (GM) structures occurring in COVID-19 "long haulers". Advanced imaging techniques can quantify brain volume depletions related to COVID-19 infection which is important as conventional Brain MRI often fails to identify disease correlates. 3-dimensional voxel-based morphometry (3D VBM) analyzes, segments and quantifies key brain volumes allowing comparisons between COVID-19 "long haulers" and normative data drawn from healthy controls, with values based on percentages of intracranial volume.

METHODS

This is a retrospective single center study which analyzed 24 consecutive COVID-19 infected patients with long term neurologic symptoms. Each patient underwent Brain MRI with 3D VBM at median time of 85 days following laboratory confirmation. All patients had relatively mild respiratory symptoms not requiring oxygen supplementation, hospitalization, or assisted ventilation. 3D VBM was obtained for whole brain and forebrain parenchyma, cortical grey matter (CGM), hippocampus, and thalamus.

RESULTS

The results demonstrate a statistically significant depletion of CGM volume in 24 COVID-19 infected patients. Reduced CGM volume likely influences their long term neurological sequelae and may impair post COVID-19 patient's quality of life and productivity.

CONCLUSION

This study contributes to understanding effects of COVID-19 infection on patient's neurocognitive and neurological function, with potential for producing serious long term personal and economic consequences, and ongoing challenges to public health systems.

Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence

PURPOSE

MRI is integral to the diagnosis of multiple sclerosis (MS) and is important for clinical prognostication. Quantitative volumetric reporting tools (QReports) can improve the accuracy and objectivity of MRI-based assessments. Several QReports are commercially available; however, validation can be difficult to establish and does not currently follow a common pathway. To aid evidence-based clinical decision-making, we performed a systematic review of commercial QReports for use in MS including technical details and published reports of validation and in-use evaluation.

METHODS

We categorized studies into three types of testing: technical validation, for example, comparison to manual segmentation, clinical validation by clinicians or interpretation of results alongside clinician-rated variables, and in-use evaluation, such as health economic assessment.

RESULTS

We identified 10 companies, which provide MS lesion and brain segmentation and volume quantification, and 38 relevant publications. Tools received regulatory approval between 2006 and 2020, contextualize results to normative reference populations, ranging from 620 to 8000 subjects, and require T1- and T2-FLAIR-weighted input sequences for longitudinal assessment of whole-brain volume and lesions. In MS, six QReports provided evidence of technical validation, four companies have conducted clinical validation by correlating results with clinical variables, only one has tested their QReport by clinician end-users, and one has performed a simulated in-use socioeconomic evaluation.

CONCLUSION

We conclude that there is limited evidence in the literature regarding clinical validation and in-use evaluation of commercial MS QReports with a particular lack of clinician end-user testing. Our systematic review provides clinicians and institutions with the available evidence when considering adopting a quantitative reporting tool for MS.

Journey to the other side of the brain: asymmetry in patients with chronic mild or moderate traumatic brain injury

Aim

Patients with chronic mild or moderate traumatic brain injury have some regions of brain atrophy (including cerebral white matter) but even more regions of abnormal brain enlargement (including other cerebral regions).

Hypothesis

Ipsilateral injury and atrophy cause the eventual development of contralateral compensatory hypertrophy.

Materials & methods

50 patients with mild or moderate traumatic brain injury were compared to 80 normal controls (n = 80) with respect to MRI brain volume asymmetry. Asymmetry-based correlations were used to test the primary hypothesis.

Results

The group of patients had multiple regions of abnormal asymmetry.

Conclusion

The correlational analyses supported the conclusion that acute injury to ipsilateral cerebral white matter regions caused atrophy, leading eventually to abnormal enlargement of contralateral regions due to compensatory hypertrophy.

Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions

OBJECTIVE

To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB.

MATERIALS AND METHODS

Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability.

RESULTS

Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions.

CONCLUSION

NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

Practical methods for segmentation and calculation of brain volume and intracranial volume: a guide and comparison

BACKGROUND

Accurate segmentation and calculation of total brain volume (BV) and intracranial volume (ICV) (further-volumetry) may serve various clinical tasks and research studies in neuroscience. Manual segmentation is extremely time consuming. There is a relative lack of published broad recommendations and comparisons of tools for automated volumetry, especially for users without expertise in computer science, for settings with limited resources, and when neuroimaging quality is suboptimal due to clinical circumstances. Our objective is to decrease the barrier to entry for research and clinical groups to perform volumetric cranial imaging analysis using free and reliable software tools.

METHODS

Automated volumetry from computed tomography (CT)/magnetic resonance imaging (MRI) scans was accomplished using 3D Slicer (v. 4.11.0), FreeSurfer (v. 7.1.1), and volBrain (v. 1.0) in a cohort of 39 patients with ischemic middle cerebral artery territory brain infarcts in the acute stage. Visual inspection for accuracy was also performed. Statistical analysis included coefficient of determination (R2) and Bland-Altman (B-A) plots. A multifaceted comparison between 3D Slicer, FreeSurfer, and volBrain from practical user perspective was performed to compile a list of distinguishing features.

RESULTS

BV: FreeSurfer, 3D Slicer, and volBrain provide similar estimations when high quality T1-MRI scans with 1 mm slices (3D scans) are available, whereas 3 mm and thicker slices (2D scans) introduce a dispersion in results. ICV: the most accurate volumetry is provided by 3D Slicer using CT scans. volBrain uses T1-MRIs and also provides good results which agree with 3D Slicer. Both of these methods may be more trustworthy than T1 MRI-derived FreeSurfer calculations.

CONCLUSIONS

All three studied tools of automated intracranial and brain volumetry-3D Slicer, FreeSurfer, and volBrain-are free, reliable, require no complex programming, but still have certain limitations and significant differences. Based on our investigation findings, the readers should be able to select the right volumetry tool and neuroimaging study, and then follow provided step-by-step instructions to accomplish specific volumetry tasks.

Brain Volumes and Abnormalities in Adults Born Preterm at Very Low Birth Weight

OBJECTIVES

To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls.

STUDY DESIGN

We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings. A neuroradiologist, masked to participants' prematurity status, reviewed the images for parenchymal and structural abnormalities, and FreeSurfer software 6.0 was used to conduct volumetric analyses. Data were analyzed by linear mixed models.

RESULTS

We found more structural abnormalities in very low birth weight participants than in siblings (37% vs 13%). The most common finding was periventricular leukomalacia, present in 15% of very low birth weight participants and in 3% of siblings. The very low birth weight group had smaller absolute brain volumes (-0.4 SD) and, after adjusting for estimated intracranial volume, less gray matter (-0.2 SD), larger ventricles (1.5 SD), smaller thalami (-0.6 SD), caudate nuclei (-0.4 SD), right hippocampus (-0.4 SD), and left pallidum (-0.3 SD). We saw no volume differences in total white matter (-0.04 SD; 95% CI, -0.13 to 0.09).

CONCLUSIONS

Preterm very low birth weight adults had a higher prevalence of brain abnormalities than their term-born siblings. They also had smaller absolute brain volumes, less gray but not white matter, and smaller volumes in several gray matter structures.

Automated Brain Volumetry in Patients With Memory Impairment: Comparison of Conventional and Ultrafast 3D T1-Weighted MRI Sequences Using Two Software Packages

BACKGROUND. Isotropic 3D T1-weighted imaging has long acquisition times, potentially leading to motion artifact and altered brain volume measurements. Acquisition times may be greatly shortened using an isotropic ultrafast 3D echo-planar imaging (EPI) T1-weighted sequence. OBJECTIVE. The purpose of this article was to compare automated brain volume measurements between conventional 3D T1-weighted imaging and ultrafast 3D EPI T1-weighted imaging. METHODS. This retrospective study included 36 patients (25 women, 11 men; mean age, 68.4 years) with memory impairment who underwent 3-T brain MRI. Examinations included both conventional 3D T1-weighted imaging using inversion recovery gradient-recalled echo sequence (section thickness, 1.0 mm; acquisition time, 3 minutes 4 seconds) and, in patients exhibiting motion, an isotropic ultrafast 3D EPI T1-weighted sequence (section thickness, 1.2 mm; acquisition time, 30 seconds). The 36-patient sample excluded five patients in whom severe motion artifact rendered the conventional sequence of insufficient quality for volume measurements. Automated brain volumetry was performed using NeuroQuant (version 3.0, CorTechs Laboratories) and FreeSurfer (version 7.1.1, Harvard University) software. Volume measurements were compared between sequences for nine regions in each hemisphere. RESULTS. Volumes showed substantial to almost perfect agreement between the two sequences for most regions bilaterally. However, most regions showed significant mean differences between sequences, and Bland-Altman analyses showed consistent systematic biases and wide limits of agreement (LOA). For example, for the left hemisphere using NeuroQuant, volume was significantly greater for the ultrafast sequence in four regions and significantly greater for the conventional sequence in three regions, whereas standardized effect size between sequences was moderate for four regions and large for one region. Using NeuroQuant, mean bias (ultrafast minus conventional) and 95% LOA were greatest in cortical gray matter bilaterally (-50.61 cm3 [-56.27 cm3, -44.94 cm3] for the left hemisphere; -50.02 cm3 [-54.88 cm3, -45.16 cm3] for the right hemisphere). The variation between the two sequences was observed in subset analyses of 16 patients with and 20 patients without Alzheimer disease. CONCLUSION. Brain volume measurements show significant differences and systematic biases between the conventional and ultrafast sequences. CLINICAL IMPACT. In patients in whom severe motion artifact precludes use of the conventional sequence, the ultrafast sequence may be useful to enable brain volume-try. However, the current conventional 3D T1-weighted sequence remains preferred in patients who can tolerate the standard examination.

[Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status]

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant® and NeuroGage® in Patients With Traumatic Brain Injury

Over 40 years of research have shown that traumatic brain injury affects brain volume. However, technical and practical limitations made it difficult to detect brain volume abnormalities in patients suffering from chronic effects of mild or moderate traumatic brain injury. This situation improved in 2006 with the FDA clearance of NeuroQuant®, a commercially available, computer-automated software program for measuring MRI brain volume in human subjects. More recent strides were made with the introduction of NeuroGage®, commercially available software that is based on NeuroQuant® and extends its utility in several ways. Studies using these and similar methods have found that most patients with chronic mild or moderate traumatic brain injury have brain volume abnormalities, and several of these studies found-surprisingly-more abnormal enlargement than atrophy. More generally, 102 peer-reviewed studies have supported the reliability and validity of NeuroQuant® and NeuroGage®. Furthermore, this updated version of a previous review addresses whether NeuroQuant® and NeuroGage® meet the Daubert standard for admissibility in court. It concludes that NeuroQuant® and NeuroGage® meet the Daubert standard based on their reliability, validity, and objectivity. Due to the improvements in technology over the years, these brain volumetric techniques are practical and readily available for clinical or forensic use, and thus they are important tools for detecting signs of brain injury.

Amygdala and subregion volumes are associated with photoperiod and seasonal depressive symptoms: A cross-sectional study in the UK Biobank cohort

Although seasonal changes in amygdala volume have been demonstrated in animals, seasonal differences in human amygdala subregion volumes have yet to be investigated. Amygdala volume has also been linked to depressed mood. Therefore, we hypothesised that differences in photoperiod would predict differences in amygdala or subregion volumes and that this association would be linked to depressed mood. 10,033 participants ranging in age from 45 to 79 years were scanned by MRI in a single location. Amygdala subregion volumes were obtained using automated processing and segmentation algorithms. A mediation analysis tested whether amygdala volume mediated the relationship between photoperiod and mood. Photoperiod was positively associated with total amygdala volume (p < .001). Multivariate (GLM) analyses revealed significant effects of photoperiod across all amygdala subregion volumes for both hemispheres (p < .001). Post hoc univariate regression analyses revealed significant associations of photoperiod with each amygdala subregion volume (p < .001). PLS showed the highest loadings of amygdala subregions in lateral nucleus, ABN, basal nucleus, CAT, PLN, AAA, central nucleus, cortical nucleus and medial nucleus for left hemisphere and ABN, lateral nucleus, CAT, PLN, cortical nucleus, AAA, central nucleus and medial nucleus for right hemisphere. There were no significant associations between photoperiod and mood nor between mood scores and amygdala volumes, and due to the lack of these associations, the mediation hypothesis was not supported. This study is the first to demonstrate an association between photoperiod and amygdala volume. These findings add to the evidence supporting the role of photoperiod on brain structural plasticity.

Brain myelin water fraction is associated with APOE4 allele status in patients with cognitive impairment

BACKGROUND AND PURPOSE

Apolipoprotein E4 (APOE4) is a major genetic risk factor for Alzheimer's disease. However, the effect of APOE4 status on myelin remains unclear. This study investigated the effect of APOE4 on myelin content in cognitively impaired individuals using T2* gradient echo (GRE)-based myelin water fraction (MWF) imaging.

METHODS

Between August 2017 and January 2019, we evaluated 39 cognitively impaired patients (median age, 75 years; male:female = 8:31; Alzheimer's disease: mild cognitive impairment = 11:28). We obtained brain MWF values from white matter hyperintensities (WMHs) and normal-appearing white matter (NAWM). Linear regression analysis was performed to investigate the relationship between the APOE4 status and MWF and cognitive function and MWF.

RESULTS

Among the 39 cognitively impaired patients, nine (23.1%) were APOE4 carriers and 30 (76.9%) were noncarriers. APOE4 carriers had a lower hippocampal volume than noncarriers (p = .045), but other brain volume parameters were not differed. After age adjustment, the APOE4 status was significantly associated with reduced MWF in NAWM (β = -0.310 per allele; p = .049) but not in WMH (β = -0.258 per allele; p = .113). After age adjustment, MWF in NAWM was significantly associated with Mini-Mental State Examination score (β = 0.313, p = .031).

CONCLUSIONS

T2* GRE-based MWF imaging can reveal myelin loss, particularly in NAWM, in cognitively impaired patients among APOE4 carriers. In vivo MWF in NAWM might be a novel imaging marker of Alzheimer's disease, for clarifying the interactions between the white matter and cognitive dysfunction with respect to the APOE4 status.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome

Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model’s C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities (P-value = 0.02). Adding quantitative MRI data increases the performance of a model developed to predict post-operative seizure outcomes. The distribution of the regions of interest included in the final model supports the notion that focal epilepsies are network disorders and that subtle cortical volume loss outside the surgical site influences seizure outcome.

Patients with chronic mild or moderate traumatic brain injury have abnormal longitudinal brain volume enlargement more than atrophy

Introduction

Many studies have found brain atrophy in patients with traumatic brain injury (TBI), but most of those studies examined patients with moderate or severe TBI. A few recent studies in patients with chronic mild or moderate TBI found abnormally large brain volume. Some of these studies used NeuroQuant®, FDA-cleared software for measuring MRI brain volume. It is not known if the abnormal enlargement occurs before or after injury. The purpose of the current study was to test the hypothesis that it occurs after injury.

Methods

55 patients with chronic mild or moderate TBI were compared to NeuroQuant® normal controls ( n > 4000) with respect to MRI brain volume change from before injury (time 0 [t0], estimated volume) to after injury (t1, measured volume). A subset of 36 patients were compared to the normal controls with respect to longitudinal change of brain volume after injury from t1 to t2.

Results

The patients had abnormally fast increase of brain volume for multiple brain regions, including whole brain, cerebral cortical gray matter, and subcortical regions.

Discussion

This is the first report of extensive abnormal longitudinal brain volume enlargement in patients with TBI. In particular, the findings suggested that the previously reported findings of cross-sectional brain volume abnormal enlargement were due to longitudinal enlargement after, not before, injury. Abnormal longitudinal enlargement of the posterior cingulate cortex correlated with neuropathic headaches, partially replicating a previously reported finding that was associated with neuroinflammation.

Combination of automated brain volumetry on MRI and quantitative tau deposition on THK-5351 PET to support diagnosis of Alzheimer's disease

Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).

FreeSurfer 5.3 versus 6.0: are volumes comparable? A Chronic Effects of Neurotrauma Consortium study

Automated neuroimaging methods like FreeSurfer ( https://surfer.nmr.mgh.harvard.edu/ ) have revolutionized quantitative neuroimaging analyses. Such analyses provide a variety of metrics used for image quantification, including magnetic resonance imaging (MRI) volumetrics. With the release of FreeSurfer version 6.0, it is important to assess its comparability to the widely-used previous version 5.3. The current study used data from the initial 249 participants in the ongoing Chronic Effects of Neurotrauma Consortium (CENC) multicenter observational study to compare the volumetric output of versions 5.3 and 6.0 across various regions of interest (ROI). In the current investigation, the following ROIs were examined: total intracranial volume, total white matter volume, total ventricular volume, total gray matter volume, and right and left volumes for the thalamus, pallidum, putamen, caudate, amygdala and hippocampus. Absolute ROI volumes derived from FreeSurfer 6.0 differed significantly from those obtained using version 5.3. We also employed a clinically-based evaluation strategy to compare both versions in their prediction of age-mediated volume reductions (or ventricular increase) in the aforementioned structures. Statistical comparison involved both general linear modeling (GLM) and random forest (RF) methods, where cross-validation error was significantly higher using segmentations from FreeSurfer version 5.3 versus version 6.0 (GLM: t = 4.97, df = 99, p value = 2.706e-06; RF: t = 4.85, df = 99, p value = 4.424e-06). Additionally, the relative importance of ROIs used to predict age using RFs differed between FreeSurfer versions, indicating substantial differences in the two versions. However, from the perspective of correlational analyses, fitted regression lines and their slopes were similar between the two versions, regardless of version used. While absolute volumes are not interchangeable between version 5.3 and 6.0, ROI correlational analyses appear to yield similar results, suggesting the interchangeability of ROI volume for correlational studies.

Comparison of Automated Brain Volume Measures by NeuroQuant vs. Freesurfer in Patients with Mild Cognitive Impairment: Effect of Slice Thickness

PURPOSE

This study aimed to examine the inter-method reliability and volumetric differences between NeuroQuant (NQ) and Freesurfer (FS) using T1 volume imaging sequence with different slice thicknesses in patients with mild cognitive impairment (MCI).

MATERIALS AND METHODS

This retrospective study enrolled 80 patients diagnosed with MCI at our memory clinic. NQ and FS were used for volumetric analysis of three-dimensional T1-weighted images with slice thickness of 1 and 1.2 mm. Inter-method reliability was measured with Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and effect size (ES).

RESULTS

Overall, NQ volumes were larger than FS volumes in several locations: whole brain (0.78%), cortical gray matter (5.34%), and white matter (2.68%). Volume measures by NQ and FS showed good-to-excellent ICCs with both 1 and 1.2 mm slice thickness (ICC=0.75-0.97, ES=-1.0-0.73 vs. ICC=0.78-0.96, ES=-0.9-0.77, respectively), except for putamen, pallidum, thalamus, and total intracranial volumes. The ICCs in all locations, except the putamen and cerebellum, were slightly higher with a slice thickness of 1 mm compared to those of 1.2 mm.

CONCLUSION

Inter-method reliability between NQ and FS was good-to-excellent in most regions with improvement with a 1-mm slice thickness. This finding indicates that the potential effects of slice thickness should be considered when performing volumetric measurements for cognitive impairment.

Evaluation of Reproducibility of Brain Volumetry between Commercial Software, Inbrain and Established Research Purpose Method, FreeSurfer

Background and Purpose

We aimed to determine the intermethod reproducibility between the commercial software Inbrain (MIDAS IT) and the established research-purpose method FreeSurfer, as well as the effect of MRI resolution and the pathological condition of subjects on their intermethod reproducibility.

Methods

This study included 45 healthy volunteers and 85 patients with mild cognitive impairment (MCI). In 43 of the 85 patients with MCI, three-dimensional, T1-weighted MRI data were obtained at an in-plane resolution of 1.2 mm. The data of the remaining 42 patients with MCI and the healthy volunteers were obtained at an in-plane resolution of 1.0 mm. The within-subject coefficient of variation (CoV), intraclass correlation coefficient (ICC), and effect size were calculated, and means were compared using paired t-tests. The parameters obtained at 1.0-mm and 1.2-mm resolutions in patients with MCI were compared to evaluate the effect of the in-plane resolution on the intermethod reproducibility. The parameters obtained at a 1.0-mm in-plane resolution in patients with MCI and healthy volunteers were used to analyze the effect of subject condition on intermethod reproducibility.

Results

Overall the two methods showed excellent reproducibility across all regions of the brain (CoV=0.5–3.9, ICC=0.93 to >0.99). In the subgroup of healthy volunteers, the intermethod reliability was only good in some regions (frontal, temporal, cingulate, and insular). The intermethod reproducibility was better in the 1.0-mm group than the 1.2-mm group in all regions other than the nucleus accumbens.

Conclusions

Inbrain and FreeSurfer showed good-to-excellent intermethod reproducibility for volumetric measurements. Nevertheless, some noticeable differences were found based on subject condition, image resolution, and brain region.

Expert Opinions and Recommendations for the Clinical Use of Quantitative Analysis Software for MRI-Based Brain Volumetry

치매를 비롯한 퇴행성 신경 질환의 초기 진단에 자기공명영상을 이용한 뇌 위축 평가와 정량적 용적 분석이 중요하다. 뇌 위축의 시각적 평가는 주관적으로 평가자에 따라 다른 결과를 보여주기 때문에, 객관적인 결과를 제공하면서 임상 적용도 가능한 소프트웨어의 수요와 개발이 늘어나고 있다. 이러한 임상용 소프트웨어의 실제 임상 적용은 영상 검사의 표준화가 선행되어야 하고, 개발된 소프트웨어의 검증이 반드시 필요하다. 따라서 대한신경두경부영상의학회는 뇌용적 분석 임상용 소프트웨어의 임상적 활용에 대한 의견을 제시하기 위해 전문위원회를 구성하고 현재까지 발표된 연구를 정리하였다. 그리고, 정량화 분석을 위한 영상 검사의 표준화 및 소프트웨어의 임상 적용에 대한 전문가 의견을 제시하기 위하여 공동 작업을 수행하였다. 본 종설에서는 뇌 자기공명영상의 정량화 분석의 필요성 및 배경, 정량화 분석을 위한 임상용 소프트웨어의 소개 및 기존의 표준품(reference standard)과의 진단능 비교, 영상 획득의 표준화, 분석 및 평가의 표준화, 소프트웨어의 임상 적용에 대한 전문가 의견, 제한점 및 대처 방법 등 대한신경두경부영상의학회의 전문가 권고안을 소개하는 것이 목적이다.

Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images

BACKGROUND AND PURPOSE

Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.

MATERIALS AND METHODS

A deep learning-based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls.

RESULTS

In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758-0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668-0.870. XGBoost had a sensitivity of 79% and a specificity of 80%.

CONCLUSIONS

The deep learning-based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.

Clinically Available Software for Automatic Brain Volumetry: Comparisons of Volume Measurements and Validation of Intermethod Reliability

OBJECTIVE

To compare two clinically available MR volumetry software, NeuroQuant® (NQ) and Inbrain® (IB), and examine the inter-method reliabilities and differences between them.

MATERIALS AND METHODS

This study included 172 subjects (age range, 55-88 years; mean age, 71.2 years), comprising 45 normal healthy subjects, 85 patients with mild cognitive impairment, and 42 patients with Alzheimer's disease. Magnetic resonance imaging scans were analyzed with IB and NQ. Mean differences were compared with the paired t test. Inter-method reliability was evaluated with Pearson's correlation coefficients and intraclass correlation coefficients (ICCs). Effect sizes were also obtained to document the standardized mean differences.

RESULTS

The paired t test showed significant volume differences in most regions except for the amygdala between the two methods. Nevertheless, inter-method measurements between IB and NQ showed good to excellent reliability (0.72 < r < 0.96, 0.83 < ICC < 0.98) except for the pallidum, which showed poor reliability (left: r = 0.03, ICC = 0.06; right: r = -0.05, ICC = -0.09). For the measurements of effect size, volume differences were large in most regions (0.05 < r < 6.15). The effect size was the largest in the pallidum and smallest in the cerebellum.

CONCLUSION

Comparisons between IB and NQ showed significantly different volume measurements with large effect sizes. However, they showed good to excellent inter-method reliability in volumetric measurements for all brain regions, with the exception of the pallidum. Clinicians using these commercial software should take into consideration that different volume measurements could be obtained depending on the software used.

Memory Performance and Quantitative Neuroimaging Software in Mild Cognitive Impairment: A Concurrent Validity Study

OBJECTIVE

This study examined the relationship between patient performance on multiple memory measures and regional brain volumes using an FDA-cleared quantitative volumetric analysis program - Neuroreader™.

METHOD

Ninety-two patients diagnosed with mild cognitive impairment (MCI) by a clinical neuropsychologist completed cognitive evaluations and underwent MR Neuroreader™ within 1 year of testing. Select brain regions were correlated with three widely used memory tests. Regression analyses were conducted to determine if using more than one memory measures would better predict hippocampal z-scores and to explore the added value of recognition memory to prediction models.

RESULTS

Memory performances were most strongly correlated with hippocampal volumes than other brain regions. After controlling for encoding/Immediate Recall standard scores, statistically significant correlations emerged between Delayed Recall and hippocampal volumes (rs ranging from .348 to .490). Regression analysis revealed that evaluating memory performance across multiple memory measures is a better predictor of hippocampal volume than individual memory performances. Recognition memory did not add further predictive utility to regression analyses.

CONCLUSIONS

This study provides support for use of MR Neuroreader™ hippocampal volumes as a clinically informative biomarker associated with memory performance, which is a critical diagnostic feature of MCI phenotype.

Assessment of White Matter Integrity after Pediatric Traumatic Brain Injury

White matter (WM) abnormalities, such as atrophy and hyperintensities (WMH), can be accessed via magnetic resonance imaging (MRI) after pediatric traumatic brain injury (TBI). Several methods are available to classify WM abnormalities (i.e., total WM volumes and WMHs), but automated and manual volumes and clinical ratings have yet to be compared in pediatric TBI. In addition, WM integrity has been associated reliably with processing speed. Consequently, methods of assessing WM integrity should relate to processing speed to have clinical application. This study had two goals: (1) to compare Scheltens rating scale, manual tracing, FreeSurfer, and NeuroQuant® methods of assessing WM abnormalities, and (2) to relate WM methods to processing speed scores. We report findings from the Social Outcomes of Brain Injury in Kids (SOBIK) study, a multi-center study of 60 children with chronic TBI (65% male) from ages 8-13. Scheltens WMH ratings had good to excellent agreement with WMH volumes for both NeuroQuant (ICC = 0.62; r = 0.29, p = 0.005) and manual tracing (ICC = 0.82; r = 0.50, p = 0.000). NeuroQuant WMH volumes did not correlate with manually traced WMH volumes (r = 0.12, p = 0.21) and had poor agreement (ICC = 0.24). NeuroQuant and FreeSurfer total WM volumes correlated (r = 0.38, p = 0.004) and had fair agreement (ICC = 0.52). The WMH assessment methods, both ratings and volumes, were associated with processing speed scores. In contrast, total WM volume was not related to processing speed. Measures of WMH may hold clinical utility for predicting cognitive functioning after pediatric TBI.

Inter-method reliability between automatic region of interest analytic application with multi-atlas segmentation and FreeSurfer

AIM

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the aggregation of amyloid-β and phosphorylated tau proteins. Magnetic resonance imaging (MRI) is a useful means of detecting hippocampal atrophy. However, instead of visual inspection, objective and time-saving tools for automated region of interest (ROI) analysis are needed. Advances in MRI segmentation techniques have enabled a multi-atlas approach with fewer errors than a conventional single-atlas approach. To support the clinical application of multi-atlas segmentation, an automated ROI analytic application consisting of multi-atlas segmentation with joint label fusion and corrective learning was developed: T-Proto. In the present study, we evaluated the inter-method reliability between T-Proto and a reference ROI analytic software, FreeSurfer.

METHODS

This was a database study. MRI data from 30 patients with AD were selected, and the inter-method reliability was assessed in terms of the intra-class correlation coefficient (ICC). A post-hoc comparison according to the severity of AD was also performed.

RESULTS

Almost all the regional volumes estimated with T-Proto were smaller than those estimated with FreeSurfer. The regional ICC values between the two methods showed moderate to excellent reliability. A post-hoc comparison revealed a similar t-value and effect size between both methods for the hippocampus.

CONCLUSION

In the present study, we showed that automated regional analysis using T-Proto was reliable in the hippocampus in terms of ICC, compared with FreeSurfer.

Brain volumes and dual-task performance correlates among individuals with cognitive impairment: a retrospective analysis

Cognitive impairment (CI) is a prevalent condition characterized by loss of brain volume and changes in cognition, motor function, and dual-tasking ability. To examine associations between brain volumes, dual-task performance, and gait and balance in those with CI to elucidate the mechanisms underlying loss of function. We performed a retrospective analysis of medical records of patients with CI and compared brain volumes, dual-task performance, and measures of gait and balance. Greater cognitive and combined dual-task effects (DTE) are associated with smaller brain volumes. In contrast, motor DTE is not associated with distinct pattern of brain volumes. As brain volumes decrease, dual-task performance becomes more motor prioritized. Cognitive DTE is more strongly associated with decreased performance on measures of gait and balance than motor DTE. Decreased gait and balance performance are also associated with increased motor task prioritization. Cognitive DTE appears to be more strongly associated with decreased automaticity and gait and balance ability than motor DTE and should be utilized as a clinical and research outcome measure in this population. The increased motor task prioritization associated with decreased brain volume and function indicates a potential for accommodative strategies to maximize function in those with CI. Counterintuitive correlations between motor brain volumes and motor DTE in our study suggest a complicated interaction between brain pathology and function.

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders

A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

BACKGROUND AND PURPOSE

Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations.

METHODS

Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV).

RESULTS

The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data.

CONCLUSIONS

MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.

Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software

OBJECTIVE

To assess the predictive ability of regional volume information provided by fully automated brain segmentation software for cerebral amyloid positivity in mild cognitive impairment (MCI).

METHODS

This study included 130 subjects with amnestic MCI who participated in the Korean brain aging study of early diagnosis and prediction of Alzheimer's disease, an ongoing prospective cohort. All participants underwent comprehensive clinical assessment as well as ¹¹C-labeled Pittsburgh compound PET/MRI scans. The predictive ability of volumetric results provided by automated brain segmentation software was evaluated using binary logistic regression and receiver operating characteristic curve analysis.

RESULTS

Subjects were divided into two groups: one with Aβ deposition (58 subjects) and one without Aβ deposition (72 subjects). Among the varied volumetric information provided, the hippocampal volume percentage of intracranial volume (%HC/ICV), normative percentiles of hippocampal volume (HC_norm), and gray matter volume were associated with amyloid-β (Aβ) positivity (all P < 0.01). Multivariate analyses revealed that both %HC/ICV and HC_norm were independent significant predictors of Aβ positivity (all P < 0.001). In addition, prediction scores derived from %HC/ICV with age and HC_norm showed moderate accuracy in predicting Aβ positivity in MCI subjects (the areas under the curve: 0.739 and 0.723, respectively).

CONCLUSION

Relative hippocampal volume measures provided by automated brain segmentation software can be useful for screening cerebral Aβ positivity in clinical practice for patients with amnestic MCI. The information may also help clinicians interpret structural MRI to predict outcomes and determine early intervention for delaying the progression to Alzheimer's disease dementia.

Inter-method and anatomical correlates of episodic memory tests in the Alzheimer's Disease spectrum

Background

Episodic memory impairments have been described as initial clinical findings in the Alzheimer’s Disease (AD) spectrum, which could be associated with the presence of early hippocampal dysfunction. However, correlates between performances in neuropsychological tests and hippocampal volumes in AD were inconclusive in the literature. Divergent methods to assess episodic memory have been depicted as a major source of heterogeneity across studies.

Methods

We examined correlates among performances in three different delayed-recall tasks (Rey-Auditory Verbal-Learning Test–RAVLT, Logical Memory and Visual Reproduction subtests from the Wechsler Memory Scale) and fully-automated volumetric measurements of the hippocampus (estimated using Neuroquant^®) of 83 older subjects (47 controls, 27 Mild Cognitive Impairment individuals and 9 participants with Dementia due to AD).

Results

Inter-method correlations of episodic memory performances were at most moderate. Scores in the RAVLT predicted up to 48% of variance in HOC (Hippocampal Occupancy Score) among subjects in the AD spectrum.

Discussion

Tests using different stimuli (verbal or visual) and presenting distinct designs (word list, story or figure learning) may assess divergent aspects in episodic memory, with heterogeneous anatomical correlates.

Conclusions

Different episodic memory tests might not assess the same construct and should not be used interchangeably. Scores in RAVLT may correlate with the presence of neurodegeneration in AD.

Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes

Objective

To rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy.

Methods

Forty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer.

Results

FreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant.

Interpretation

Automated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur.

Evaluation of the intra- and inter-method agreement of brain MRI segmentation software packages: A comparison between SPM12 and FreeSurfer v6.0

PURPOSE

The lack of inter-method agreement can produce inconsistent results in neuroimaging studies. We evaluated the intra-method repeatability and the inter-method reproducibility of two widely-used automatic segmentation methods for brain MRI: the FreeSurfer (FS) and the Statistical Parametric Mapping (SPM) software packages.

METHODS

We segmented the gray matter (GM), the white matter (WM) and subcortical structures in test-retest MRI data of healthy volunteers from Kirby-21 and OASIS datasets. We used Pearson's correlation (r), Bland-Altman plot and Dice index to study intra-method repeatability and inter-method reproducibility. In order to test whether different processing methods affect the results of a neuroimaging-based group study, we carried out a statistical comparison between male and female volume measures.

RESULTS

A high correlation was found between test-retest volume measures for both SPM (r in the 0.98-0.99 range) and FS (r in the 0.95-0.99 range). A non-null bias between test-retest FS volumes was detected for GM and WM in the OASIS dataset. The inter-method reproducibility analysis measured volume correlation values in the 0.72-0.98 range and the overlap between the segmented structures assessed by the Dice index was in the 0.76-0.83 range. SPM systematically provided significantly greater GM volumes and lower WM and subcortical volumes with respect to FS. In the male vs. female brain volume comparisons, inconsistencies arose for the OASIS dataset, where the gender-related differences appear subtler with respect to the Kirby dataset.

CONCLUSIONS

The inter-method reproducibility should be evaluated before interpreting the results of neuroimaging studies.

Brain regional volume estimations with NeuroQuant and FIRST: a study in patients with a clinically isolated syndrome

PURPOSE

Brain volume estimates from magnetic resonance images (MRIs) are of great interest in multiple sclerosis, and several automated tools have been developed for this purpose. The goal of this study was to assess the agreement between two tools, NeuroQuant® (NQ) and FMRIB's Integrated Registration Segmentation Tool (FIRST), for estimating overall and regional brain volume in a cohort of patients with a clinically isolated syndrome (CIS). In addition, white matter lesion volume was estimated with NQ and the Lesion Segmentation Toolbox (LST).

METHODS

One hundred fifteen CIS patients were analysed. Structural images were acquired on a 3.0-T system. The volume agreement between methods (by estimation of the intraclass correlation coefficient) was calculated for the right and left thalamus, caudate, putamen, pallidum, hippocampus, and amygdala, as well as for the total intracranial volume and white matter lesion volume.

RESULTS

In general, the estimated volumes were larger by NQ than FIRST, except for the pallidum. Agreement was low (ICC < 0.40) for the smaller structures (amygdala and pallidum) and fair to good (ICC > 0.40) for the remaining ones. Agreement was fair for lesion volume (ICC = 0.61), with NQ estimates lower than LST.

CONCLUSIONS

Agreement between NQ and FIRST brain volume estimates depends on the size of the structure of interest, with larger volumes achieving better agreement. In addition, concordance between the two tools does seem to be dependent on the presence of brain lesions.

Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years?

To quantify the "segmentation noise" of several widely used fully automatic methods for measuring longitudinal hippocampal atrophy in Alzheimer's disease and compare the results to the segmentation noise of manual segmentation over both 1 and 3 years. The segmentation noise of 5 longitudinal hippocampal atrophy measurement methods was quantified, including checking its Gaussianity, using 264 subjects from the ADNI1 back-to-back (BTB) data set over both 1 year and 3 year intervals. The segmentation methods were FreeSurfer 5.3.0 both cross sectional and longitudinal, FreeSurfer 6.0.0 longitudinal, MAPS-HBSI and FSL/FIRST 5.0.8. The BTB manual segmentation of 75 ADNI subjects from a previous study provided the manual distributions for comparison. All methods, including the manual segmentation, violated the Gaussianity assumption. Two methods, FreeSurfer 6.0.0 and MAPS-HBSI, had a segmentation noise substantially less than a surrogate for manual segmentation. FreeSurfer 5.3.0 longitudinal was confirmed as a surrogate for manual segmentation. The violation of the Gaussian assumption by the segmentation methods assessed, including manual, suggests results of previous studies that assumed Gaussian statistics without confirmation may need review. Fully automatic FreeSurfer 6.0.0 and MAPS-HBSI both have lower segmentation noise than manual requiring less than two thirds of the subjects to detect the same treatment effect.