Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework

Jain, Saurabh; Ribbens, Annemie; Sima, Diana M.; Cambron, Melissa; Keyser, Jacques De; Wang, Chenyu; Barnett, Michael; Huffel, Sabine Van; Maes, Frederik; Smeets, Dirk

doi:10.3389/fnins.2016.00576

Cited by 38 publications

(46 citation statements)

References 22 publications

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“…Several researchers have proposed methods to harmonize segmentations across two or more timepoints. Jain et al propose a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation, and the Lesion Segmentation Toolkit, a tool integrated in SPM, has a longitudinal pipeline which adapts existing segmentations across multiple timepoints [12,13].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

McKinley

Wepfer

Grunder

et al. 2020

NeuroImage: Clinical

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The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter-and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper we demonstrate that change in volumetric measurements of lesion load alone is not a good method for performing this separation, even for highly performing segmentation methods. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on a second external dataset confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracy of 83 % in separating stable and progressive timepoints.Both lesion volume and lesion count have previously been shown to be, together with clinical covariates, strong predictors of disease course across a population. However, in this paper we demonstrate that for individual patients, changes in these measures are not an adequate means of establishing no evidence of disease activity. Meanwhile, directly detecting tissue which changes, with high confidence, from non-lesion to lesion is a feasible methodology for identifying radiologically active patients.

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Section: Introductionmentioning

confidence: 99%

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

McKinley

Wepfer

Grunder

et al. 2020

NeuroImage: Clinical

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“…Two of the eight tools (ADABOOST 35 and Qure 25 ) are designed to specifically perform a single type of analysis (hippocampus segmentation and gross abnormality identification, respectively). On the contrary, the other six tools (DIADEM 36,37 , Icobrain [38][39][40][41] , Jung Diagnostics 27,42,43 , Neu-roQuant 24,[44][45][46][47][48][49][50][51] , Quantib 52,53 , volBrain 54,55 ) are designed to extract multiple types of information from the data and/or evaluate multiple disorders.…”

Section: Included Toolsmentioning

confidence: 99%

“…In particular, five tools are designed to provide quantitative support to the diagnosis of dementia and in particular of Alzheimer's disease (ADABOOST 35 , Jung Diagnostistics 27,43 , NeuroQuant 45 , Quantib 53 , volBrain 26,54 ), mild cognitive impairment (MCI) (ADABOOST 35 , Jung Diagnostics 27 , NeuroQuant 48 ), or other forms of dementia (Jung Diagnostics 43 ). Furthermore, four tools are designed to support the diagnosis of MS (Icobrain 38,39,41 , Jung Diagnostics 42 , Quantib 52 , volBrain 55 ). In addition, one tool (NeuroQuant) has a parallel version called LesionQuant which has been developed to assist the n/a n/a n/a n/a n/a n/a n/a n/a Cardoso et al…”

Section: Target Disordersmentioning

confidence: 99%

“…Similarly, the main pathognomonic feature for MS is the presence of inflammatory WM lesions 57 . Accordingly, five tools are designed to perform the segmentation of WM lesions and to calculate their volume (Icobrain 38,39 , JungDiagnostic 42 , NeuroQuant-no reference available, Quantib 52 , volBrain 55 ). In addition, as MS has recently been described to be associated with GM atrophy, one tool (Icobrain 41 ) also provides atrophy measurements in patients with MS.…”

Section: Type Of Analysismentioning

confidence: 99%

“…The vast majority of the identified tools analyze magnetic resonance images (MRI) data, in particular, T1-weighted images (ADABOOST 35 , Icobrain 38,39,41 , Jung Diagnostics 27,42,43 , NeuroQuant 44,45,47,48 , Quantib 52,53 , Vol-Brain 26,54,55 ). However, there are a few exceptions.…”

Section: Brain Imaging Typementioning

confidence: 99%

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

Scarpazza

Baecker

et al. 2020

Transl Psychiatry

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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.

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A Multicenter Longitudinal MRI Study Assessing LeMan‐PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients

Todea

Melie‐García

Baraković

et al. 2023

Magnetic Resonance Imaging

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BackgroundDetecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan‐PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow‐up of MS patients; however, multicenter validation studies are lacking.PurposeTo assess the accuracy of LeMan‐PV for the longitudinal detection NEL white‐matter MS lesions in a multicenter clinical setting.Study TypeRetrospective, longitudinal.SubjectsA total of 206 patients with a definitive MS diagnosis and at least two follow‐up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow‐up = 45.2 years (range: 36.9–52.8 years); 70 males.Field Strength/SequenceFluid attenuated inversion recovery (FLAIR) and T1‐weighted magnetization prepared rapid gradient echo (T1‐MPRAGE) sequences at 1.5 T and 3 T.AssessmentThe study included 313 MRI pairs of datasets. Data were analyzed with LeMan‐PV and compared with a manual “reference standard” provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating‐accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1‐score, lesion‐wise False‐Positive‐Rate (aFPR), and other measures were used to assess LeMan‐PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.Statistical TestsIntraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.ResultsThe interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10−20, CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10−12, CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan‐PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1‐score = 0.44, aFPR = 1.31. When both follow‐ups were acquired at 3 T, LeMan‐PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1‐score = 0.28, aFPR = 3.03).Data ConclusionIn this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan‐PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan‐PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological‐routine flow.Evidence Level4Technical EfficacyStage 2

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Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework

Cited by 38 publications

References 22 publications

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

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

A Multicenter Longitudinal MRI Study Assessing LeMan‐PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients

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