Dual-Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI

Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review

BACKGROUND

Segmentation tools continue to advance, evolving from manual contouring to deep learning. Researchers have utilized segmentation to study a myriad of posterior fossa-related conditions, such as Chiari malformation, trigeminal neuralgia, post-operative pediatric cerebellar mutism syndrome, and Crouzon syndrome. Herein, we present a summary of the current literature on segmentation of the posterior fossa. The review highlights the various segmentation techniques, and their respective strengths and weaknesses, employed along with objectives and outcomes of the various studies reported in the literature.

METHODS

A literature search was conducted in PubMed, Embase, Cochrane, and Web of Science up to November 2023 for articles on segmentation techniques of posterior fossa. The two senior authors searched through databases based on the keywords of the article separately and then enrolled joint articles that met the inclusion and exclusion criteria.

RESULTS

The initial search identified 2205 articles. After applying inclusion and exclusion criteria, 77 articles were selected for full-text review after screening of titles/abstracts. 52 articles were ultimately included in the review. Segmentation techniques included manual, semi-automated, and fully automated (atlas-based, convolutional neural networks). The most common pathology investigated was Chiari malformation.

CONCLUSIONS

Various forms of segmentation techniques have been used to assess posterior fossa volumes/pathologies and each has its advantages and disadvantages. We discuss these nuances and summarize the current state of literature in the context of posterior fossa-associated pathologies.

Multiple sclerosis lesions that impair memory map to a connected memory circuit

BACKGROUND

Nearly 1 million Americans are living with multiple sclerosis (MS) and 30-50% will experience memory dysfunction. It remains unclear whether this memory dysfunction is due to overall white matter lesion burden or damage to specific neuroanatomical structures. Here we test if MS memory dysfunction is associated with white matter lesions to a specific brain circuit.

METHODS

We performed a cross-sectional analysis of standard structural images and verbal memory scores as assessed by immediate recall trials from 431 patients with MS (mean age 49.2 years, 71.9% female) enrolled at a large, academic referral center. White matter lesion locations from each patient were mapped using a validated algorithm. First, we tested for associations between memory dysfunction and total MS lesion volume. Second, we tested for associations between memory dysfunction and lesion intersection with an a priori memory circuit derived from stroke lesions. Third, we performed mediation analyses to determine which variable was most associated with memory dysfunction. Finally, we performed a data-driven analysis to derive de-novo brain circuits for MS memory dysfunction using both functional (n = 1000) and structural (n = 178) connectomes.

RESULTS

Both total lesion volume (r = 0.31, p < 0.001) and lesion damage to our a priori memory circuit (r = 0.34, p < 0.001) were associated with memory dysfunction. However, lesion damage to the memory circuit fully mediated the association of lesion volume with memory performance. Our data-driven analysis identified multiple connections associated with memory dysfunction, including peaks in the hippocampus (T = 6.05, family-wise error p = 0.000008), parahippocampus, fornix and cingulate. Finally, the overall topography of our data-driven MS memory circuit matched our a priori stroke-derived memory circuit.

CONCLUSIONS

Lesion locations associated with memory dysfunction in MS map onto a specific brain circuit centered on the hippocampus. Lesion damage to this circuit fully mediated associations between lesion volume and memory. A circuit-based approach to mapping MS symptoms based on lesions visible on standard structural imaging may prove useful for localization and prognosis of higher order deficits in MS.

Increasing Neurofilament and Glial Fibrillary Acidic Protein After Treatment Discontinuation Predicts Multiple Sclerosis Disease Activity

BACKGROUND AND OBJECTIVES

Stable patients with multiple sclerosis (MS) may discontinue treatment, but the risk of disease activity is unknown. Serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) are biomarkers of subclinical disease activity and may help risk stratification. In this study, sNfL and sGFAP levels in stable patients were evaluated before and after treatment discontinuation to determine association with disease activity.

METHODS

This observational study included patients enrolled in the Comprehensive Longitudinal Investigation in MS at the Brigham and Women's Hospital who discontinued treatment after >2 years disease activity-free. Two serum samples within 2 years, before and after treatment stop, were sent for sNfL and sGFAP measurements by single-molecule array. Biannual neurologic examinations and yearly MRI scans determined disease activity by 3 time-to-event outcomes: 6-month confirmed disability worsening (CDW), clinical attacks, and MRI activity (new T2 or contrast-enhancing lesions). Associations between each outcome and log-transformed sNfL and sGFAP levels pretreatment stop and posttreatment stop and the percent change were estimated using multivariable Cox regression analysis adjusting for age, disability, disease duration, and duration from attack before treatment stop.

RESULTS

Seventy-eight patients (92% female) discontinued treatment at a median (interquartile range) age of 48.5 years (39.0-55.7) and disease duration of 12.3 years (7.5-18.8) and were followed up for 6.3 years (4.2-8.5). CDW occurred in 27 patients (35%), new attacks in 19 (24%), and new MRI activity in 26 (33%). Higher posttreatment stop sNfL level was associated with CDW (adjusted hazard ratio (aHR) 2.80, 95% CI 1.36-5.76, p = 0.005) and new MRI activity (aHR 3.09, 95% CI 1.42-6.70, p = 0.004). Patients who had >100% increase in sNfL level from pretreatment stop to posttreatment stop had greater risk of CDW (HR 3.87, 95% CI 1.4-10.7, p = 0.009) and developing new MRI activity (HR 4.02, 95% CI 1.51-10.7, p = 0.005). Patients who had >50% increase in sGFAP level also had greater risk of CDW (HR 5.34, 95% CI 1.4-19.9, p = 0.012) and developing new MRI activity (HR 5.16, 95% CI 1.71-15.6, p = 0.004).

DISCUSSION

Stable patients who discontinue treatment may be risk stratified by sNfL and sGFAP levels measured before and after discontinuing treatment. Further studies are needed to validate findings and determine whether resuming treatment in patients with increasing biomarker levels reduces risk of subsequent disease activity.

Early miR-320b and miR-25-3p miRNA levels correlate with multiple sclerosis severity at 10 years: a cohort study

BACKGROUND

Multiple sclerosis (MS) is a chronic demyelinating autoimmune disorder which may cause long-term disability. MicroRNA (miRNA) are stable, non-coding molecules that have been identified in our Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB)-cohort, as well as other international cohorts, as potential disease biomarkers in MS. However, few studies have evaluated the association of miRNA expression early in the MS disease course with long-term outcomes. Therefore, we aimed to evaluate the potential role of three candidate serum miRNAs previously correlated with MS disability in patients with MS, miR-320b, miR-25-3p and miRNA 486-5p, as early biomarkers of MS disability at 10-year follow-up.

MAIN BODY

We included 144 patients with serum obtained within three years of MS onset. miRNA expression was measured by RNA extraction followed by RT-PCR. Demographic, clinical, brain MRI and other biomarkers were collected. The primary outcome was the association between early miRNA expression and retaining benign MS, defined as EDSS ≤ 2 at 10-year follow-up. Among the 144 patients, 104 were benign and 40 were not benign at 10-year follow-up. 89 (62%) were women, with mean age at onset 37.7 (SD: 9.6) years. Patients who retained benign MS had lower values of miR-25-3p (p = 0.047) and higher miR-320b (p = 0.025) values. Development of SPMS was associated with higher miR-320b (p = 0.002) levels. Brain parenchymal fraction at year 10 was negatively correlated with miR-25-3p (p = 0.0004) and positively correlated with miR-320b (p = 0.006). No association was found between miR-486-5p and any outcome, and 10-year T2-lesion volume was not associated with any miRNA.

CONCLUSIONS

Our results show that miR-320b and miR-25-3p expression are early biomarkers associated with MS severity and brain atrophy. This study provides class III evidence of that miR-320b and miR-25-3p are associated with long-term MS disability which may be a potential tool to risk-stratify patients with MS for early treatment decisions.

Early neurofilament light and glial fibrillary acidic protein levels improve predictive models of multiple sclerosis outcomes

BACKGROUND

Early risk-stratification in multiple sclerosis (MS) may impact treatment decisions. Current predictive models have identified that clinical and imaging characteristics of aggressive disease are associated with worse long-term outcomes. Serum biomarkers, neurofilament (sNfL) and glial fibrillary acidic protein (sGFAP), reflect subclinical disease activity through separate pathological processes and may contribute to predictive models of clinical and MRI outcomes.

METHODS

We conducted a retrospective analysis of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB study), where patients with multiple sclerosis are seen every 6 months and undergo Expanded Disability Status Scale (EDSS) assessment, have annual brain MRI scans where volumetric analysis is conducted to calculate T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and donate a yearly blood sample for subsequent analysis. We included patients with newly diagnosed relapsing-remitting MS and serum samples obtained at baseline visit and 1-year follow-up (both within 3 years of onset), and were assessed at 10-year follow-up. We measured sNfL and sGFAP by single molecule array at baseline visit and at 1-year follow-up. A predictive clinical model was developed using age, sex, Expanded Disability Status Scale (EDSS), pyramidal signs, relapse rate, and spinal cord lesions at first visit. The main outcome was odds of developing of secondary progressive (SP)MS at year 10. Secondary outcomes included 10-year EDSS, brain T2LV and BPF. We compared the goodness-of-fit of the predictive clinical model with and without sNfL and sGFAP at baseline and 1-year follow-up, for each outcome by area under the receiver operating characteristic curve (AUC) or R-squared.

RESULTS

A total 144 patients with median MS onset at age 37.4 years (interquartile range: 29.4-45.4), 64% female, were included. SPMS developed in 25 (17.4%) patients. The AUC for the predictive clinical model without biomarker data was 0.73, which improved to 0.77 when both sNfL and sGFAP were included in the model (P = 0.021). In this model, higher baseline sGFAP associated with developing SPMS (OR=3.3 [95%CI:1.1,10.6], P = 0.04). Adding 1-year follow-up biomarker levels further improved the model fit (AUC = 0.79) but this change was not statistically significant (P = 0.15). Adding baseline biomarker data also improved the R-squared of clinical models for 10-year EDSS from 0.24 to 0.28 (P = 0.032), while additional 1-year follow-up levels did not. Baseline sGFAP was associated with 10-year EDSS (ß=0.58 [95%CI:0.00,1.16], P = 0.05). For MRI outcomes, baseline biomarker levels improved R-squared for T2LV from 0.12 to 0.27 (P<0.001), and BPF from 0.15 to 0.20 (P = 0.042). Adding 1-year follow-up biomarker data further improved T2LV to 0.33 (P = 0.0065) and BPF to 0.23 (P = 0.048). Baseline sNfL was associated with T2LV (ß=0.34 [95%CI:0.21,0.48], P<0.001) and 1-year follow-up sNfL with BPF (ß=-2.53% [95%CI:-4.18,-0.89], P = 0.003).

CONCLUSIONS

Early biomarker levels modestly improve predictive models containing clinical and MRI variables. Worse clinical outcomes, SPMS and EDSS, are associated with higher sGFAP levels and worse MRI outcomes, T2LV and BPF, are associated with higher sNfL levels. Prospective study implementing these predictive models into clinical practice are needed to determine if early biomarker levels meaningfully impact clinical practice.

Fronto-striatal damage may contribute to resistance to fatigue-lowering medications in multiple sclerosis

BACKGROUND AND PURPOSE

Commonly used fatigue-lowering medications have not been proven effective in treating multiple sclerosis (MS)-related fatigue. A neuroanatomical basis for treatment-resistant fatigue in MS has not been explored. The aim of this study was to investigate the association between brain diffusion abnormality patterns and resistance to fatigue-lowering treatment.

METHODS

Retrospective patient stratification: 1. treatment-resistant (n = 22) received anti-fatigue and/or anti-depressant and/or anxiolytic medication and the latest two Modified Fatigue Impact Scale (MFIS) score≥38; 2. responder (n = 16): received anti-fatigue and/or antidepressant and/or anxiolytic medication while the latest MFIS was <38, and minimum one previous MFIS was ≥38; 3. non-treated never-fatigued (n = 26): received none of the above-mentioned medications and MFIS was always<38 (over minimum four years assessed with MFIS every 1-2 years). 3T brain MRI was used to perform a cross-sectional voxel-wise comparison of fractional anisotropy (FA) between the groups.

RESULTS

Treatment-resistant versus responder patients showed more extensive brain damage (ie, lower FA) favoring the fronto-striatal pathways. Both groups showed more widespread brain damage than non-treated never-fatigued patients. A mean fronto-striatal FA value of 0.26 could perfectly predict response to modafinil/armodafinil.

CONCLUSION

Fronto-striatal damage may play a role in the development of resistance to fatigue-lowering treatment. Fronto-striatal FA may serve as a biomarker to predict response to fatigue-lowering medications in MS.

Automatic detection of active and inactive multiple sclerosis plaques using the Bayesian approach in susceptibility-weighted imaging

BACKGROUND

Susceptibility-weighted imaging (SWI) is efficient in detecting multiple sclerosis (MS) plaques and evaluating the level of disease activity.

PURPOSE

To automatically detect active and inactive MS plaques in SWI images using a Bayesian approach.

MATERIAL AND METHODS

A 1.5-T scanner was used to evaluate 147 patients with MS. The area of the plaques along with their active or inactive status were automatically identified using a Bayesian approach. Plaques were given an orange color if they were active and a blue color if they were inactive, based on the preset signal intensity.

RESULTS

Experimental findings show that the proposed method has a high accuracy rate of 91% and a sensitivity rate of 76% for identifying the type and area of plaques. Inactive plaques were properly identified in 87% of cases, and active plaques in 76% of cases. The Kappa analysis revealed an 80% agreement between expert diagnoses based on contrast-enhanced and FLAIR images and Bayesian inferences in SWI.

CONCLUSION

The results of our study demonstrated that the proposed method has good accuracy for identifying the MS plaque area as well as for identifying the types of active or inactive plaques in SWI. Therefore, it might be helpful to use the proposed method as a supplemental tool to accelerate the specialist's diagnosis.

Younger age at multiple sclerosis onset is associated with worse outcomes at age 50

OBJECTIVE

Older age at multiple sclerosis (MS) onset has been associated with worse 10-year outcomes. However, disease duration often exceeds 10 years and age-related comorbidities may also contribute to disability. We investigated patients with>10 years disease duration to determine how age at MS onset is associated with clinical, MRI and occupational outcomes at age 50.

METHODS

We included patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital with disease duration>10 years. Outcomes at age 50 included the Expanded Disability Status Scale (EDSS), development of secondary-progressive multiple sclerosis (SPMS), brain T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and occupational status. We assessed how onset age was independently associated with each outcome when adjusting for the date of visit closest to age 50, sex, time to first treatment, number of treatments by age 50 and exposure to high-efficacy treatments by age 50.

RESULTS

We included 661 patients with median onset at 31.4 years. The outcomes at age 50 were worse the younger first symptoms developed: for every 5 years earlier, the EDSS was 0.22 points worse (95% CI: 0.04 to 0.40; p=0.015), odds of SPMS 1.33 times higher (95% CI: 1.08 to 1.64; p=0.008), T2LV 1.86 mL higher (95% CI: 1.02 to 2.70; p<0.001), BPF 0.97% worse (95% CI: 0.52 to 1.42; p<0.001) and odds of unemployment from MS 1.24 times higher (95% CI: 1.01 to 1.53; p=0.037).

CONCLUSIONS

All outcomes at age 50 were worse in patients with younger age at onset. Decisions to provide high-efficacy treatments should consider younger age at onset, equating to a longer expected disease duration, as a poor prognostic factor.

MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke

Neuroimaging biomarkers that predict the edema after acute stroke may help clinicians provide targeted therapies and minimize the risk of secondary injury. In this study, we applied pretherapy MRI radiomics features from infarction and cerebrospinal fluid (CSF) to predict edema after acute ischemic stroke. MRI data were obtained from a prospective, endovascular thrombectomy (EVT) cohort that included 389 patients with acute stroke from two centers (dataset 1, n = 292; dataset 2, n = 97), respectively. Patients were divided into edema group (brain swelling and midline shift) and non-edema group according to CT within 36 h after therapy. We extracted the imaging features of infarct area on diffusion weighted imaging (DWI) (abbreviated as DWI), CSF on fluid-attenuated inversion recovery (FLAIR) (CSF_FLAIR) and CSF on DWI (CSF_DWI), and selected the optimum features associated with edema for developing models in two forms of feature sets (DWI + CSF_FLAIR and DWI + CSF_DWI) respectively. We developed seven ML models based on dataset 1 and identified the most stable model. External validations (dataset 2) of the developed stable model were performed. Prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC). The Bayes model based on DWI + CSF_FLAIR and the RF model based on DWI + CSF_DWI had the best performances (DWI + CSF_FLAIR: AUC, 0.86; accuracy, 0.85; recall, 0.88; DWI + CSF_DWI: AUC, 0.86; accuracy, 0.84; recall, 0.84) and the most stability (RSD% in DWI + CSF_FLAIR AUC: 0.07, RSD% in DWI + CSF_DWI AUC: 0.09), respectively. External validation showed that the AUC of the Bayes model based on DWI + CSF_FLAIR was 0.84 with accuracy of 0.77 and area under precision-recall curve (auPRC) of 0.75, and the AUC of the RF model based on DWI + CSF_DWI was 0.83 with accuracy of 0.81 and the auPRC of 0.76. The MRI radiomics features from infarction and CSF may offer an effective imaging biomarker for predicting edema.

Serum NfL levels in the first five years predict 10-year thalamic fraction in patients with MS

Background

Serum neurofilament light chain (sNfL) levels are associated with relapses, MRI lesions, and brain volume in multiple sclerosis (MS).

Objective

To explore the value of early serum neurofilament light (sNfL) measures in prognosticating 10-year regional brain volumes in MS.

Methods

Patients with MS enrolled in the Comprehensive Longitudinal Investigations in MS at Brigham and Women's Hospital (CLIMB) study within five years of disease onset who had annual blood samples from years 1–10 (n = 91) were studied. sNfL was measured with a single molecule array (SIMOA) assay. We quantified global cortical thickness and normalized deep gray matter (DGM) volumes (fractions of the thalamus, caudate, putamen, and globus pallidus) from high-resolution 3 T MRI at 10 years. Correlations between yearly sNfL levels and 10-year MRI outcomes were assessed using linear regression models.

Results

sNfL levels from years 1 and 2 were associated with 10-year thalamus fraction. Early sNfL levels were not associated with 10-year putamen, globus pallidus or caudate fractions. At 10 years, cortical thickness was not associated with early sNfL levels, but was weakly correlated with total DGM fraction.

Conclusions

Early sNfL levels correlate with 10-year thalamic volume, supporting its role as a prognostic biomarker in MS.

Infratentorial lesions in multiple sclerosis patients: intra- and inter-rater variability in comparison to a fully automated segmentation using 3D convolutional neural networks

OBJECTIVE

Automated quantification of infratentorial multiple sclerosis lesions on magnetic resonance imaging is clinically relevant but challenging. To overcome some of these problems, we propose a fully automated lesion segmentation algorithm using 3D convolutional neural networks (CNNs).

METHODS

The CNN was trained on a FLAIR image alone or on FLAIR and T1-weighted images from 1809 patients acquired on 156 different scanners. An additional training using an extra class for infratentorial lesions was implemented. Three experienced raters manually annotated three datasets from 123 MS patients from different scanners.

RESULTS

The inter-rater sensitivity (SEN) was 80% for supratentorial lesions but only 62% for infratentorial lesions. There was no statistically significant difference between the inter-rater SEN and the SEN of the CNN with respect to the raters. For supratentorial lesions, the CNN featured an intra-rater intra-scanner SEN of 0.97 (R1 = 0.90, R2 = 0.84) and for infratentorial lesion a SEN of 0.93 (R1 = 0.61, R2 = 0.73).

CONCLUSION

The performance of the CNN improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input and matches the detection performance of experienced raters. Furthermore, for infratentorial lesions the CNN was more robust against repeated scans than experienced raters.

KEY POINTS

• A 3D convolutional neural network was trained on MRI data from 1809 patients (156 different scanners) for the quantification of supratentorial and infratentorial multiple sclerosis lesions. • Inter-rater variability was higher for infratentorial lesions than for supratentorial lesions. The performance of the 3D convolutional neural network (CNN) improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input. • The detection performance of the CNN matches the detection performance of experienced raters.

Relapse recovery in multiple sclerosis: Effect of treatment and contribution to long-term disability

Background

Although recovery from relapses in MS appears to contribute to disability, it has largely been ignored as a treatment endpoint and disability predictor.

Objective

To identify demographic and clinical predictors of relapse recovery in the first 3 years and examine its contribution to 10-year disability and MRI outcomes.

Methods

Relapse recovery was retrospectively assessed in 360 patients with MS using the return of the Expanded Disability Status Scale (EDSS), Functional System Scale and neurologic signs to baseline at least 6 months after onset. Univariate and multivariable models were used to associate recovery with demographic and clinical factors and predict 10-year outcomes.

Results

Recovery from relapses in the first 3 years was better in patients who were younger, on disease-modifying treatment, with a longer disease duration and without bowel or bladder symptoms. For every incomplete recovery, 10-year EDSS increased by 0.6 and 10-year timed 25-foot walk increased by 0.5 s. These outcomes were also higher with older age and higher baseline BMI. Ten-year MRI brain atrophy was associated only with older age, and MRI lesion volume was only associated with smoking.

Conclusions

Early initiation of disease-modifying treatment in MS was associated with improved relapse recovery, which in turn prevented long-term disability.

Gut Microbiome in Progressive Multiple Sclerosis

OBJECTIVE

This study was undertaken to investigate the gut microbiome in progressive multiple sclerosis (MS) and how it relates to clinical disease.

METHODS

We sequenced the microbiota from healthy controls and relapsing-remitting MS (RRMS) and progressive MS patients and correlated the levels of bacteria with clinical features of disease, including Expanded Disability Status Scale (EDSS), quality of life, and brain magnetic resonance imaging lesions/atrophy. We colonized mice with MS-derived Akkermansia and induced experimental autoimmune encephalomyelitis (EAE).

RESULTS

Microbiota β-diversity differed between MS patients and controls but did not differ between RRMS and progressive MS or differ based on disease-modifying therapies. Disease status had the greatest effect on the microbiome β-diversity, followed by body mass index, race, and sex. In both progressive MS and RRMS, we found increased Clostridium bolteae, Ruthenibacterium lactatiformans, and Akkermansia and decreased Blautia wexlerae, Dorea formicigenerans, and Erysipelotrichaceae CCMM. Unique to progressive MS, we found elevated Enterobacteriaceae and Clostridium g24 FCEY and decreased Blautia and Agathobaculum. Several Clostridium species were associated with higher EDSS and fatigue scores. Contrary to the view that elevated Akkermansia in MS has a detrimental role, we found that Akkermansia was linked to lower disability, suggesting a beneficial role. Consistent with this, we found that Akkermansia isolated from MS patients ameliorated EAE, which was linked to a reduction in RORγt+ and IL-17-producing γδ T cells.

INTERPRETATION

Whereas some microbiota alterations are shared in relapsing and progressive MS, we identified unique bacteria associated with progressive MS and clinical measures of disease. Furthermore, elevated Akkermansia in MS may be a compensatory beneficial response in the MS microbiome. ANN NEUROL 2021;89:1195-1211.

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

OBJECTIVES

Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.

DESIGN

Scoping review.

SETTING

Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.

RESULTS

Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.

CONCLUSIONS

The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

Regional microglial activation in the substantia nigra is linked with fatigue in MS

OBJECTIVE

The goal of our study is to assess the role of microglial activation in MS-associated fatigue (MSAF) using [F-18]PBR06-PET.

METHODS

Fatigue severity was measured using the Modified Fatigue Impact Scale (MFIS) in 12 subjects with MS (7 relapsing-remitting and 5 secondary progressive) and 10 healthy control participants who underwent [F-18]PBR06-PET. The MFIS provides a total fatigue score as well as physical, cognitive, and psychosocial fatigue subscale scores. Standardized Uptake Value (SUV) 60-90 minute frame PET maps were coregistered to 3T MRI. Voxel-by-voxel analysis using Statistical Parametric Mapping and atlas-based regional analyses were performed. SUV ratios (SUVRs) were global brain normalized.

RESULTS

Peak voxel-based level of significance for correlation between total fatigue score and PET uptake was localized to the right substantia nigra (T-score 4.67, p = 0.001). Similarly, SUVRs derived from atlas-based segmentation of the substantia nigra showed significant correlation with MFIS (r = 0.76, p = 0.004). On multiple regression, the right substantia nigra was an independent predictor of total MFIS (p = 0.02) and cognitive MFIS subscale values (p = 0.007), after adjustment for age, disability, and depression. Several additional areas of significant correlations with fatigue scores were identified, including the right parahippocampal gyrus, right precuneus, and juxtacortical white matter (all p < 0.05). There was no correlation between fatigue scores and brain atrophy and lesion load in patients with MS.

CONCLUSION

Substantia nigra microglial activation is linked to fatigue in MS. Microglial activation across key brain regions may represent a unifying mechanism for MSAF, and further evaluation of neuroimmunologic basis of MSAF is warranted.

Faster RCNN-based detection of cervical spinal cord injury and disc degeneration

Magnetic resonance imaging (MRI) can indirectly reflect microscopic changes in lesions on the spinal cord; however, the application of deep learning to MRI to classify and detect lesions for cervical spinal cord diseases has not been sufficiently explored. In this study, we implemented a deep neural network for MRI to detect lesions caused by cervical diseases. We retrospectively reviewed the MRI of 1,500 patients irrespective of whether they had cervical diseases. The patients were treated in our hospital from January 2013 to December 2018. We randomly divided the MRI data into three groups of datasets: disc group (800 datasets), injured group (200 datasets), and normal group (500 datasets). We designed the relevant parameters and used a faster-region convolutional neural network (Faster R-CNN) combined with a backbone convolutional feature extractor using the ResNet-50 and VGG-16 networks, to detect lesions during MRI. Experimental results showed that the prediction accuracy and speed of Faster R-CNN with ResNet-50 and VGG-16 in detecting and recognizing lesions from a cervical spinal cord MRI were satisfactory. The mean average precisions (mAPs) for Faster R-CNN with ResNet-50 and VGG-16 were 88.6 and 72.3%, respectively, and the testing times was 0.22 and 0.24 s/image, respectively. Faster R-CNN can identify and detect lesions from cervical MRIs. To some extent, it may aid radiologists and spine surgeons in their diagnoses. The results of our study can provide motivation for future research to combine medical imaging and deep learning.

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

A novel classification of fatigue in multiple sclerosis based on longitudinal assessments

BACKGROUND

Magnetic resonance imaging (MRI) studies of multiple sclerosis-related fatigue had limited reproducibility. Temporal fatigue fluctuations have not been considered.

OBJECTIVE

To investigate whether a novel group allocation that reflects temporal dynamics of fatigue improves our ability to detect fatigue-associated structural brain abnormalities.

METHODS

Patient stratification based on biennial fatigue assessments: sustained fatigue (SF, n = 29, fatigued at the latest ⩾2 assessments), one time-point fatigue (1F, n = 15, fatigued at the latest, but non-fatigued at the penultimate assessment), reversible fatigue (RF, n = 31, non-fatigued at the latest assessment, but reported fatigue previously), and never fatigued (NF, n = 54). Brain parenchymal fraction (BPF) and T2 lesion volume (T2LV) were compared between these groups and were derived using a conventional, single time-point fatigued versus non-fatigued stratification.

RESULTS

The SF versus NF stratification yielded improved power. SF (p = 0.005) and RF (p = 0.043) showed significantly higher T2LV than NF. T2LV showed no significant differences in SF versus 1F, SF versus RF, or 1F versus RF. Fatigued versus non-fatigued patients showed significantly higher T2LV (p = 0.030). We found no significant differences in BPF between the groups.

CONCLUSION

Taking into account temporal fatigue dynamics increases the statistical power with respect to T2LV and may improve characterization of brain pathological correlates of MS-related fatigue.

Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices

In this paper, we present a fully convolutional densely connected network (Tiramisu) for multiple sclerosis (MS) lesion segmentation. Different from existing methods, we use stacked slices from all three anatomical planes to achieve a 2.5D method. Individual slices from a given orientation provide global context along the plane and the stack of adjacent slices adds local context. By taking stacked data from three orientations, the network has access to more samples for training and can make more accurate segmentation by combining information of different forms. The conducted experiments demonstrated the competitive performance of our method. For an ablation study, we simulated lesions on healthy controls to generate images with ground truth lesion masks. This experiment confirmed that the use of 2.5D patches, stacked data and the Tiramisu model improve the MS lesion segmentation performance. In addition, we evaluated our approach on the Longitudinal MS Lesion Segmentation Challenge. The overall score of 93.1 places the L 2-loss variant of our method in the first position on the leaderboard, while the focal-loss variant has obtained the best Dice coefficient and lesion-wise true positive rate with 69.3% and 60.2%, respectively.

Gray matter microglial activation in relapsing vs progressive MS: A [F-18]PBR06-PET study

Objective

To determine the value of [F-18]PBR06-PET for assessment of microglial activation in the cerebral gray matter in patients with MS.

Methods

Twelve patients with MS (7 relapsing-remitting and 5 secondary progressive [SP]) and 5 healthy controls (HCs) had standardized uptake value (SUV) PET maps coregistered to 3T MRI and segmented into cortical and subcortical gray matter regions. SUV ratios (SUVRs) were global brain normalized. Voxel-by-voxel analysis was performed using statistical parametric mapping (SPM). Normalized brain parenchymal volumes (BPVs) were determined from MRI using SIENAX.

Results

Cortical SUVRs were higher in the hippocampus, amygdala, midcingulate, posterior cingulate, and rolandic operculum and lower in the medial-superior frontal gyrus and cuneus in the MS vs HC group (all p < 0.05). Subcortical gray matter SUVR was higher in SPMS vs RRMS (+10.8%, p = 0.002) and HC (+11.3%, p = 0.055) groups. In the MS group, subcortical gray matter SUVR correlated with the Expanded Disability Status Scale (EDSS) score (r = 0.75, p = 0.005) and timed 25-foot walk (T25FW) (r = 0.70, p = 0.01). Thalamic SUVRs increased with increasing EDSS scores (r = 0.83, p = 0.0008) and T25FW (r = 0.65, p = 0.02) and with decreasing BPV (r = -0.63, p = 0.03). Putaminal SUVRs increased with increasing EDSS scores (0.71, p = 0.009) and with decreasing BPV (r = -0.67, p = 0.01). On SPM analysis, peak correlations of thalamic voxels with BPV were seen in the pulvinar and with the EDSS score and T25FW in the dorsomedial thalamic nuclei.

Conclusions

This study suggests that [F-18]PBR06-PET detects widespread abnormal microglial activation in the cerebral gray matter in MS. Increased translocator protein binding in subcortical gray matter regions is associated with brain atrophy and may link to progressive MS.

Microstructural fronto-striatal and temporo-insular alterations are associated with fatigue in patients with multiple sclerosis independent of white matter lesion load and depression

BACKGROUND

Fatigue in multiple sclerosis (MS) has been inconsistently associated with disruption of specific brain circuitries. Temporal fluctuations of fatigue have not been considered.

OBJECTIVE

The aim of this study was to investigate the association of fatigue with brain diffusion abnormalities, using robust criteria for patient stratification based on longitudinal patterns of fatigue.

METHODS

Patient stratification: (1) sustained fatigue (SF, n = 26): latest two Modified Fatigue Impact Scale (MFIS) ⩾ 38; (2) reversible fatigue (RF, n = 25): latest MFIS < 38 and minimum one previous MFIS ⩾ 38; and (3) never fatigued (NF, n = 42): MFIS always < 38 (five assessments minimum). 3T brain magnetic resonance imaging (MRI) was used to perform voxel-wise comparison of fractional anisotropy (FA) between the groups controlling for age, sex, disease duration, physical disability, white matter lesion load (T2LV), and depression.

RESULTS

SF and, to a lesser extent, RF patients showed lower FA in multiple brain regions compared to NF patients, independent of age, sex, disease duration, and physical disability. In cingulo-postcommissural-striato-thalamic regions, the differences in FA between SF and NF (but not between RF and NF or SF) patients were independent of T2LV, and in ventromedial prefronto-precommissuro-striatal and temporo-insular areas, independent of T2LV and depression.

CONCLUSION

Damage to ventromedial prefronto-precommissuro-striatal and temporo-insular pathways appears to be a specific substrate of SF in MS.

The impact of cervical spinal cord atrophy on quality of life in multiple sclerosis

BACKGROUND

Spinal cord demyelination is common in multiple sclerosis (MS) and has been linked to increased disability and progressive clinical course. Spinal cord atrophy shows an especially close relationship to MS-related physical disability, though the relationship between spinal cord lesions/atrophy and health-related quality of life (QOL) has not been explored.

METHODS

62 patients (53 relapsing MS, 7 secondary progressive, 2 clinically isolated syndrome) from our center underwent 3 T MRI within 30 days of clinical examination and QOL assessment. Upper cervical (C1-C3) spinal cord area (UCCA) was obtained from 3D high-resolution MPRAGE sequences (1 mm isotropic voxels). Cervical spinal cord (C1-C7) lesion count, and cervical and brain T2 hyperintense lesion volumes were calculated. Brain parenchymal fraction (BPF) was obtained from an automated segmentation pipeline. Spearman correlations were assessed between MRI and clinical data. Partial Spearman correlations adjusting for age, disease duration, and BPF assessed the independent association between MRI variables and QOL domains.

RESULTS

UCCA showed an inverse relationship with age (r = -0.330, p = .009), disease duration, (r = -0.444, p < .001), and nine-hole peg test (r = -0.353, p = .005). The Upper Extremity Function QOL domain showed the strongest relationship to UCCA (r = 0.333, p = .008), with Lower Extremity Function QOL (r = 0.234, p = .067) and Satisfaction with Social Roles and Activities (r = 0.245, p = .055) correlations bordering significance. The association between UCCA and Upper Extremity QOL remained significant after adjustment for BPF, age, and disease duration. QOL domains reflective of psychological health (Depression, Anxiety, Emotional and Behavioral Dyscontrol, Positive Affect and Wellbeing) showed no relationship to UCCA. Cervical and brain lesion volume related to impairment in Stigma while cervical lesion count was unrelated to NeuroQOL impairment. Brain atrophy correlated with conventional markers of disability and cognition but did not have a significant relationship to QOL.

CONCLUSION

Cervical spinal cord volume is independently associated with impaired upper extremity-related QOL in patients with MS. These findings suggest specific clinical relevance of MS-related spinal cord atrophy as compared to brain or cervical spinal cord lesions, or whole brain atrophy.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

Whole brain and deep gray matter atrophy detection over 5 years with 3T MRI in multiple sclerosis using a variety of automated segmentation pipelines

Background

Cerebral atrophy is common in multiple sclerosis (MS) and selectively involves gray matter (GM). Several fully automated methods are available to measure whole brain and regional deep GM (DGM) atrophy from MRI.

Objective

To assess the sensitivity of fully automated MRI segmentation pipelines in detecting brain atrophy in patients with relapsing-remitting (RR) MS and normal controls (NC) over five years.

Methods

Consistent 3D T1-weighted sequences were performed on a 3T GE unit in 16 mildly disabled patients with RRMS and 16 age-matched NC at baseline and five years. All patients received disease-modifying immunotherapy on-study. Images were applied to two pipelines to assess whole brain atrophy [brain parenchymal fraction (BPF) from SPM12; percentage brain volume change (PBVC) from SIENA] and two other pipelines (FSL-FIRST; FreeSurfer) to assess DGM atrophy (thalamus, caudate, globus pallidus, putamen). MRI change was compared by two sample t-tests. Expanded Disability Status Scale (EDSS) and timed 25-foot walk (T25FW) change was compared by repeated measures proportional odds models.

Results

Using FreeSurfer, the MS group had a ~10-fold acceleration in on-study volume loss than NC in the caudate (mean decrease 0.51 vs. 0.05 ml, p = 0.022). In contrast, caudate atrophy was not detected by FSL-FIRST (mean decrease 0.21 vs. 0.12 ml, p = 0.53). None of the other pipelines showed any difference in volume loss between groups, for whole brain or regional DGM atrophy (all p>0.38). The MS group showed on-study stability on EDSS (p = 0.47) but slight worsening of T25FW (p = 0.054).

Conclusions

In this real-world cohort of mildly disabled treated patients with RRMS, we identified ongoing atrophy of the caudate nucleus over five years, despite the lack of any significant whole brain atrophy, compared to healthy controls. The detectability of caudate atrophy was dependent on the MRI segmentation pipeline employed. These findings underscore the increased sensitivity gained when assessing DGM atrophy in monitoring MS.

Relationships among clinic, home, and ambulatory blood pressures with small vessel disease of the brain and functional status in older people with hypertension

BACKGROUND

Subcortical small vessel disease, represented as white matter hyperintensity (WMH) on magnetic resonance images (MRI) is associated with functional decline in older people with hypertension. We evaluated the relationships of clinic and out-of-office blood pressures (BP) with WMH and functional status in older persons.

METHODS

Using cross-sectional data from 199 older study participants enrolled in the INFINITY trial, we analyzed the clinic, 24-hour ambulatory, and home BPs and their relationships with WMH burden and mobility and cognitive outcomes.

RESULTS

Volume of WMH was associated with clinic and 24-hour ambulatory systolic BP but not home systolic BP. The mobility measure, supine-to-sit time, had a significant association with 24-hour systolic BP and pulse pressure but not with diastolic BP or values obtained by home BP. Cognitive measures of processing speed (Trails Making Test Part A and the Stroop Word Test) were significantly associated with 24-hour systolic BP, but not clinic and home BPs.

CONCLUSION

These data demonstrate that ambulatory BP measurements in older people are more strongly associated with WMH and certain measures of functional status compared to home BP measurements. Hence, home BP may not be a useful substitute for ambulatory BP for assessing subcortical small vessel disease and its consequences. Further longitudinal analyses comparing clinic and various types of out-of-office BP measures with small vessel brain disease are needed. Clinicaltrials.gov identifier: NCT01650402.

Neurofilament light chain serum levels correlate with 10-year MRI outcomes in multiple sclerosis

Objective

To assess the value of annual serum neurofilament light (NfL) measures in predicting 10‐year clinical and MRI outcomes in multiple sclerosis (MS).

Methods

We identified patients in our center's Comprehensive Longitudinal Investigations in MS at Brigham and Women's Hospital (CLIMB) study enrolled within 5 years of disease onset, and with annual blood samples up to 10 years (n = 122). Serum NfL was measured using a single molecule array (SIMOA) assay. An automated pipeline quantified brain T2 hyperintense lesion volume (T2LV) and brain parenchymal fraction (BPF) from year 10 high‐resolution 3T MRI scans. Correlations between averaged annual NfL and 10‐year clinical/MRI outcomes were assessed using Spearman's correlation, univariate, and multivariate linear regression models.

Results

Averaged annual NfL values were negatively associated with year 10 BPF, which included averaged year 1–5 NfL values (unadjusted P < 0.01; adjusted analysis P < 0.01), and averaged values through year 10. Linear regression analyses of averaged annual NfL values showed multiple associations with T2LV, specifically averaged year 1–5 NfL (unadjusted P < 0.01; adjusted analysis P < 0.01). Approximately 15–20% of the BPF variance and T2LV could be predicted from early averaged annual NfL levels. Also, averaged annual NfL levels with fatigue score worsening between years 1 and 10 showed statistically significant associations. However, averaged NfL measurements were not associated with year 10 EDSS, SDMT or T25FW in this cohort.

Interpretation

Serum NfL measured during the first few years after the clinical onset of MS contributed to the prediction of 10‐year MRI brain lesion load and atrophy.

A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the volume of hyperintense lesions on T2-weighted images (T2L). Unfortunately, T2L are non-specific for the level of tissue destruction and show a weak relationship to clinical status. Interest in lesions that appear hypointense on T1-weighted images (T1L) ("black holes") has grown because T1L provide more specificity for axonal loss and a closer link to neurologic disability. The technical difficulty of T1L segmentation has led investigators to rely on time-consuming manual assessments prone to inter- and intra-rater variability. This study aims to develop an automatic T1L segmentation approach, adapted from a T2L segmentation algorithm.

MATERIALS AND METHODS

T1, T2, and fluid-attenuated inversion recovery (FLAIR) sequences were acquired from 40 MS subjects at 3 Tesla (3 T). T2L and T1L were manually segmented. A Method for Inter-Modal Segmentation Analysis (MIMoSA) was then employed.

RESULTS

Using cross-validation, MIMoSA proved to be robust for segmenting both T2L and T1L. For T2L, a Sørensen-Dice coefficient (DSC) of 0.66 and partial AUC (pAUC) up to 1% false positive rate of 0.70 were achieved. For T1L, 0.53 DSC and 0.64 pAUC were achieved. Manual and MIMoSA segmented volumes were correlated and resulted in 0.88 for T1L and 0.95 for T2L. The correlation between Expanded Disability Status Scale (EDSS) scores and manual versus automatic volumes were similar for T1L (0.32 manual vs. 0.34 MIMoSA), T2L (0.33 vs. 0.32), and the T1L/T2L ratio (0.33 vs 0.33).

CONCLUSIONS

Though originally designed to segment T2L, MIMoSA performs well for segmenting T1 black holes in patients with MS.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions.

METHODS

Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking.

RESULTS

In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset.

CONCLUSION

MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study.