Self-organizing maps as a tool for segmentation of Magnetic Resonance Imaging (MRI) of relapsing-remitting multiple sclerosis

Multiple Sclerosis (MS) is the most prevalent demyelinating disease of the Central Nervous System, being the Relapsing-Remitting (RRMS) its most common subtype. We explored here the viability of use of Self Organizing Maps (SOM) to perform automatic segmentation of MS lesions apart from CNS normal tissue. SOM were able, in most cases, to successfully segment MRIs of patients with RRMS, with the correct separation of normal versus pathological tissue especially in supratentorial acquisitions, although it could not differentiate older from newer lesions.

[1]  Simon J. Doran,et al.  Autoencoder in Time-Series Analysis for Unsupervised Tissues Characterisation in a Large Unlabelled Medical Image Dataset , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[2]  Teuvo Kohonen,et al.  Visual Explorations in Finance , 1998 .

[3]  R. Iman,et al.  Rank Transformations as a Bridge between Parametric and Nonparametric Statistics , 1981 .

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[6]  Fraser.  A New Method for Data Integration and Integrated Data Interpretation : Self-Organising Maps , 2007 .

[7]  R. Reynolds,et al.  Normal-appearing white matter in multiple sclerosis is in a subtle balance between inflammation and neuroprotection. , 2007, Brain : a journal of neurology.

[8]  F. Jacques.  Defining the clinical course of multiple sclerosis: The 2013 revisions , 2015, Neurology.

[9]  Vijayakumar Chinnadurai,et al.  Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps , 2007, Comput. Medical Imaging Graph..

[10]  Baxter P. Rogers,et al.  Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Maps , 2013, IEEE Transactions on Biomedical Engineering.

[11]  Fabiano Reis,et al.  Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps , 2015, Journal of the Neurological Sciences.

[12]  Thierry Coléou,et al.  Interpreter's Corner—Unsupervised seismic facies classification: A review and comparison of techniques and implementation , 2003 .

[13]  Pradip M. Pattany,et al.  Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs , 2011 .

[14]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[15]  R. Reynolds,et al.  Molecular Changes in Normal Appearing White Matter in Multiple Sclerosis are Characteristic of Neuroprotective Mechanisms Against Hypoxic Insult , 2003, Brain pathology.

[17]  Yan Li,et al.  MR Brain Image Segmentation Based on Self-Organizing Map Network , 2005 .

[18]  Michael J. Brammer,et al.  Group analysis of self-organizing maps based on functional MRI using restricted Frechet means , 2012, NeuroImage.

[19]  Giancarlo Valente,et al.  Multiclass fMRI data decoding and visualization using supervised self-organizing maps , 2014, NeuroImage.

[20]  Olivier Clatz,et al.  Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images , 2010, MICCAI.

[21]  S. Fraser,et al.  Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon , 2012 .

[22]  Michelle Chaves Kuroda,et al.  Interpretation of seismic multiattributes using a neural network , 2012 .