Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

Cervell; Imatge per ressonància magnètica; Aprenentatge transductiuCerebro; Imagen de resonancia magnética; Aprendizaje transductivoBrain; Magnetic resonance imaging; Transductive learningSegmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.KK holds FI-DGR2017 grant from the Catalan Government with reference number 2017FI_B00372. This work has been supported by DPI2017-86696-R from the Ministerio de Ciencia y Tecnologia

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis

Imaging; Multiple sclerosis; Quantitative MRIImatges; Esclerosi múltiple; Ressonància magnètica quantitativaImágenes; Esclerosis múltiple; Resonancia magnética cuantitativaQuantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.C.G. is supported by the Swiss National Science Foundation (SNSF) grant PP00P3_176984, the Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung and the EUROSTAR E! 113682 HORIZON2020. F.B. is supported by the National Institute for Health Research biomedical research center at University College London Hospitals. J.W. is supported by the EU Horizon2020 research and innovation grant (FORCE, 668039). D.S.R. is supported by the Intramural Research Program of National Institute of Neurological Disorders and Stroke, National Institutes of Health. A.T.T. is supported by an Medical Research Council grant (MR/S026088/1). S.R. is supported by the Austrian Science Foundation (FWF) grant I-3001. P.S. is supported by the Intramural Research Program of National Institute of Neurological Disorders and Stroke, National Institutes of Health. H.V. is supported by the Dutch multiple sclerosis Research Foundation, ZonMW and HealthHolland

Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis

MRI; Deep learning; Multiple sclerosisResonancia magnética; Aprendizaje profundo; Esclerosis múltipleRessonància magnètica; Aprenentatge profund; Esclerosi múltipleThe assessment of disease activity using serial brain MRI scans is one of the most valuable strategies for monitoring treatment response in patients with multiple sclerosis (MS) receiving disease-modifying treatments. Recently, several deep learning approaches have been proposed to improve this analysis, obtaining a good trade-off between sensitivity and specificity, especially when using T1-w and T2-FLAIR images as inputs. However, the need to acquire two different types of images is time-consuming, costly and not always available in clinical practice. In this paper, we investigate an approach to generate synthetic T1-w images from T2-FLAIR images and subsequently analyse the impact of using original and synthetic T1-w images on the performance of a state-of-the-art approach for longitudinal MS lesion detection. We evaluate our approach on a dataset containing 136 images from MS patients, and 73 images with lesion activity (the appearance of new T2 lesions in follow-up scans). To evaluate the synthesis of the images, we analyse the structural similarity index metric and the median absolute error and obtain consistent results. To study the impact of synthetic T1-w images, we evaluate the performance of the new lesion detection approach when using (1) both T2-FLAIR and T1-w original images, (2) only T2-FLAIR images, and (3) both T2-FLAIR and synthetic T1-w images. Sensitivities of 0.75, 0.63, and 0.81, respectively, were obtained at the same false-positive rate (0.14) for all experiments. In addition, we also present the results obtained when using the data from the international MSSEG-2 challenge, showing also an improvement when including synthetic T1-w images. In conclusion, we show that the use of synthetic images can support the lack of data or even be used instead of the original image to homogenize the contrast of the different acquisitions in new T2 lesions detection algorithms.AC holds an FPI grant from the Ministerio de Ciencia, Innovación y Universidades with reference number PRE2018-083507. This work has been supported by DPI2020-114769RB-I00 from the Ministerio de Ciencia, Innovación y Universidades. The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN X GPU used in this research. This work has been also supported by ICREA Academia Program

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

Harmonization; MRI; Multiple sclerosisHarmonització; Ressonància magnètica; Esclerosi múltipleArmonización; Resonancia magnética; Esclerosis múltipleThere is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources

Recommendations for the Diagnosis and Treatment of Multiple Sclerosis Relapses

Metilprednisolona; Esclerosi múltiple; RecaigudaMetilprednisolona; Esclerosis múltiple; RecaídaMethylprednisolone; Multiple sclerosis; RelapseMinimizing the risk of relapse is essential in multiple sclerosis (MS). As none of the treatments currently available are capable of completely preventing relapses, treatment of these episodes remains a cornerstone of MS care. The objective of this manuscript is to reduce uncertainty and improve quality of care of this neurological process. This article addresses definitions of key concepts, recommendations for clinical examination, classification criteria, magnetic resonance imaging, biomarkers, and specific therapeutic counsels including special populations such as pregnant and breastfeeding women, and children. An algorithm for treating MS relapses is also provided.This research was funded by NOVARTIS FARMACÉUTICA, SA, for the two expert panel meetings held and the recording and transcription of the content of the first meeting performed by the contract research organization, Dynamic

MR imaging findings in primary spinal cord glioblastoma

Tumors glials; Tumors primaris de medul·la espinal; Glioblastoma de la medul·la espinalTumores gliales; Tumores primarios de la médula espinal; Glioblastoma de la médula espinalGlial tumors; Primary spinal cord tumors; Spinal cord glioblastomaSpinal cord glioblastoma is a rare disease, with an aggressive course and a poor prognosis. We describe magnetic resonance imaging (MRI) findings, in 3 adult cases of biopsy-confirmed glioblastoma. Conventional MRI findings were unclear with regard to the differential diagnosis between this rare tumor and other more common spinal cord lesions, including less aggressive tumors such as ependymoma or pilocytic astrocytoma, abscesses or tumefactive demyelinating lesions. After reasonable exclusion of infectious/inflammatory conditions, a final diagnosis of glioblastoma was established based on histopathological analysis. The cases reported reflect the difficulty of early radiological diagnosis of spinal cord glioblastoma, and indicate the need to perform a biopsy once inflammatory-infectious conditions are excluded with appropriate laboratory tests

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

Automatic lesion segmentation; Convolutional neural networks; Multiple sclerosisSegmentació automàtica de les lesions ; Xarxes neuronals convolucionals; Esclerosi múltipleSegmentación automática de las lesiones ; Redes neuronales convolucionales; Esclerosis múltipleIn recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling

Paramagnetic rims are a promising diagnostic imaging biomarker in multiple sclerosis

MRI; Multiple sclerosis; BiomarkersRessonància magnètica; Esclerosi múltiple; BiomarcadorsResonancia magnética; Esclerosis múltiple; BiomarcadoresBackground: White matter lesions (WMLs) on brain magnetic resonance imaging (MRI) in multiple sclerosis (MS) may contribute to misdiagnosis. In chronic active lesions, peripheral iron-laden macrophages appear as paramagnetic rim lesions (PRLs). Objective: To evaluate the sensitivity and specificity of PRLs in differentiating MS from mimics using clinical 3T MRI scanners. Method: This retrospective international study reviewed MRI scans of patients with MS (n = 254), MS mimics (n = 91) and older healthy controls (n = 217). WMLs, detected using fluid-attenuated inversion recovery MRI, were analysed with phase-sensitive imaging. Sensitivity and specificity were assessed for PRLs. Results: At least one PRL was found in 22.9% of MS and 26.1% of clinically isolated syndrome (CIS) patients. Only one PRL was found elsewhere. The identification of ⩾1 PRL was the optimal cut-off and had high specificity (99.7%, confidence interval (CI) = 98.20%–99.99%) when distinguishing MS and CIS from mimics and healthy controls, but lower sensitivity (24.0%, CI = 18.9%–36.6%). All patients with a PRL showing a central vein sign (CVS) in the same lesion (n = 54) had MS or CIS, giving a specificity of 100% (CI = 98.8%–100.0%) but equally low sensitivity (21.3%, CI = 16.4%–26.81%) Conclusion: PRLs may reduce diagnostic uncertainty in MS by being a highly specific imaging diagnostic biomarker, especially when used in conjunction with the CVS

SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

Esclerosi múltiple; Classificació d'aprenentatge automàtic; Selecció de funcionsEsclerosis múltiple; Clasificación de aprendizaje automático; Selección de característicasMultiple sclerosis; Machine learning classification; Feature selectionMachine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.This project received funding from the European Union's Horizon2020 Research and Innovation Program EuroPOND under grant agreement number 666992, and it was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. We thank all participating partners of the MAGNIMS study group for sharing their data with us

Lesiones cerebrales captantes de gadolinio en el brote de los pacientes con esclerosis múltiple

Esclerosis múltiple; Brote; Resonancia magnéticaEsclerosi múltiple; Brot; Imatge per ressonància magnèticaMultiple sclerosis; Outbreak; Magnetic resonance imagingObjective To study the clinico-radiological paradox in multiple sclerosis (MS) relapse by analyzing the number and location of gadolinium-enhanced (Gd+) lesions on brain MRI before methylprednisolone (MP) treatment. Methods We analyzed brain MRI from 90 relapsed MS patients in two Phase IV multicenter double-blind randomized clinical trials that showed the noninferiority of different routes and doses of MP administration. A 1.5- or 3-T brain MRI was performed at baseline before MP treatment and within 15 days of symptom onset. The number and location of Gd+ lesions were analyzed. Associations were studied using univariate analysis. Results Sixty-two percent of patients had at least 1 Gd+ brain lesion; the median number was 1 (interquartile range 0–4), and 41% of patients had 2 or more lesions. The most frequent location of Gd+ lesions was subcortical (41.4%). Gd+ brain lesions were found in 71.4% of patients with brainstem-cerebellum symptoms, 57.1% with spinal cord symptoms and 55.5% with optic neuritis (ON). Thirty percent of patients with brain symptoms did not have Gd+ lesions, and only 43.6% of patients had symptomatic Gd+ lesions. The univariate analysis showed a negative correlation between age and the number of Gd+ lesions (p = 0.002). Conclusion Most patients with relapse showed several Gd+ lesions on brain MRI, even when the clinical manifestation was outside of the brain. Our findings illustrate the clinico-radiological paradox in MS relapse and support the value of brain MRI in this scenario.Objetivo Estudiar la paradoja clínico-radiológica en el brote de la esclerosis múltiple (EM) mediante el análisis de lesiones captantes de gadolinio (Gd+) en la RM cerebral antes del tratamiento con metilprednisolona (MP). Métodos Analizamos la RM cerebral basal de 90 pacientes con EM en brote de 2 ensayos clínicos aleatorizados multicéntricos fase IV que demostraron la no inferioridad de diferentes vías y dosis de MP, realizadas antes del tratamiento con MP y en los 15 días siguientes a la aparición de los síntomas. Se analizaron el número y la localización de las lesiones Gd+. Se estudiaron las asociaciones mediante análisis univariado. Resultados El 62% de los pacientes tenía al menos una lesión Gd+ cerebral y el 41% de los pacientes tenía 2 o más lesiones. La localización más frecuente fue la subcortical (41,4%). Se encontraron lesiones Gd+ cerebrales en el 71,4% de los pacientes con síntomas de tronco cerebral o cerebelo, en el 57,1% con síntomas medulares y en el 55,5% con neuritis óptica. El 30% de los pacientes con síntomas cerebrales no tenían lesiones Gd+ y sólo el 4,.6% de los pacientes tenían lesiones Gd+ sintomáticas. El análisis univariante mostró una correlación negativa entre la edad y el número de lesiones Gd+ (p = 0,002). Conclusiones La mayoría de los pacientes en brote mostraron varias lesiones Gd+ en la RM cerebral, incluso cuando la manifestación clínica fue medular u óptica. Nuestros hallazgos ilustran la paradoja clínico-radiológica en el brote de la EM y apoyan el valor de la RM cerebral en este escenario.This work was supported in part by the Ministry of Health of Spain (grant numbers EC07/90278 and EC11/132) and personal grant Rio Hortega CM19/00042 to LMA