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

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.

[1]  Peter A. Calabresi,et al.  Longitudinal multiple sclerosis lesion segmentation data resource , 2017, Data in brief.

[2]  S. Kanekar.  Imaging of white matter lesions. , 2014, Radiologic clinics of North America.

[3]  John Muschelli,et al.  brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data , 2014, R J..

[4]  Alex Rovira,et al.  Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches , 2012, Inf. Sci..

[5]  Thomas Lengauer,et al.  ROCR: visualizing classifier performance in R , 2005, Bioinform..

[6]  Christoph Meinel,et al.  Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.

[7]  S D Walter,et al.  The partial area under the summary ROC curve , 2005, Statistics in medicine.

[8]  Aaron Carass,et al.  Simple paradigm for extra-cerebral tissue removal: Algorithm and analysis , 2011, NeuroImage.

[9]  Y. Ge.  Multiple sclerosis: the role of MR imaging. , 2006, AJNR. American journal of neuroradiology.

[10]  Snehashis Roy,et al.  Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation , 2015, IEEE Journal of Biomedical and Health Informatics.

[11]  John Muschelli,et al.  fslr: Connecting the FSL Software with R , 2015, R J..

[12]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[13]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[14]  Aaron Carass,et al.  Erratum to: The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software , 2010, Neuroinformatics.

[15]  Russell T. Shinohara,et al.  Subject-level measurement of local cortical coupling , 2016, NeuroImage.

[16]  Volker Schmid,et al.  Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R , 2011 .

[17]  Rohit Bakshi,et al.  Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis , 2006, Neurology.

[18]  Peter A. Calabresi,et al.  A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI , 2014, PloS one.

[19]  Snehashis Roy,et al.  Longitudinal multiple sclerosis lesion segmentation: Resource and challenge , 2017, NeuroImage.

[20]  Ramon Casanova,et al.  Biological parametric mapping: A statistical toolbox for multimodality brain image analysis , 2007, NeuroImage.

[21]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[22]  Amanda F. Mejia,et al.  Statistical estimation of white matter microstructure from conventional MRI , 2016, NeuroImage: Clinical.

[23]  C. Crainiceanu,et al.  Statistical normalization techniques for magnetic resonance imaging , 2014, NeuroImage: Clinical.

[24]  Peter A. Calabresi,et al.  OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆ , 2013, NeuroImage: Clinical.

[25]  Alex Rovira,et al.  MR in the diagnosis and monitoring of multiple sclerosis: an overview. , 2008, European journal of radiology.

[26]  Elena Marchiori,et al.  Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities , 2016, Scientific Reports.

[27]  Volker Schmid,et al.  Working with the DICOM and NIfTI Data Standards in R , 2011 .

[28]  J A Frank,et al.  Standardized MR imaging protocol for multiple sclerosis: Consortium of MS Centers consensus guidelines. , 2006, AJNR. American journal of neuroradiology.

[29]  Russell T. Shinohara,et al.  Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis , 2011, NeuroImage.

[30]  F. X. Aymerich,et al.  Interferon β-1b for the treatment of primary progressive multiple sclerosis: five-year clinical trial follow-up. , 2011, Archives of neurology.

[31]  Russell T. Shinohara,et al.  Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions , 2015, NeuroImage: Clinical.

[32]  David J. Hand,et al.  Classifier Technology and the Illusion of Progress , 2006, math/0606441.

[33]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[34]  Russell T. Shinohara,et al.  Statistical estimation of T 1 relaxation times using conventional magnetic resonance imaging , 2016, NeuroImage.

[35]  Rohit Bakshi,et al.  Dual‐Sensitivity Multiple Sclerosis Lesion and CSF Segmentation for Multichannel 3T Brain MRI , 2017, Journal of neuroimaging : official journal of the American Society of Neuroimaging.