BOOST: A supervised approach for multiple sclerosis lesion segmentation

BACKGROUND Automatic multiple sclerosis lesion segmentation is a challenging task. An extensive analysis of the most recent techniques indicates an improvement of the results obtained when using prior knowledge and contextual information. NEW METHOD We present BOOST, a knowledge-based approach to automatically segment multiple sclerosis lesions through a voxel by voxel classification. We used the Gentleboost classifier and a set of features, including contextual features, registered atlas probability maps and an outlier map. RESULTS Results are computed on a set of 45 cases from three different hospitals (15 of each), obtaining a moderate agreement between the manual annotations and the automatically segmented results. COMPARISON WITH EXISTING METHOD(S) We quantitatively compared our results with three public state-of-the-art approaches obtaining competitive results and a better overlap with manual annotations. Our approach tends to better segment those cases with high lesion load, while cases with small lesion load are more difficult to accurately segment. CONCLUSIONS We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.

[1]  Koen L. Vincken,et al.  Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.

[2]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[3]  Grégoire Malandain,et al.  An Automatic Segmentation of T2-FLAIR Multiple Sclerosis Lesions , 2008, The MIDAS Journal.

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

[5]  Ball State,et al.  Comparison of Distance Measures in Cluster Analysis with Dichotomous Data , 2004 .

[6]  Alex Rovira,et al.  Intensity Based Methods for Brain MRI Longitudinal Registration. A Study on Multiple Sclerosis Patients , 2013, Neuroinformatics.

[7]  S. Schoenberg,et al.  Influence of multichannel combination, parallel imaging and other reconstruction techniques on MRI noise characteristics. , 2008, Magnetic resonance imaging.

[8]  Meritxell Bach Cuadra,et al.  A review of atlas-based segmentation for magnetic resonance brain images , 2011, Comput. Methods Programs Biomed..

[9]  Olivier Clatz,et al.  Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images , 2011, NeuroImage.

[10]  Jordi Freixenet,et al.  Detecting Faint Compact Sources Using Local Features and a Boosting Approach , 2010, 2010 20th International Conference on Pattern Recognition.

[11]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

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

[13]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[14]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[15]  Yusuf Yaslan,et al.  Ensemble based classifiers using dictionary learning , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).

[16]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[17]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[18]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[19]  D. Louis Collins,et al.  Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images , 1995, IEEE Trans. Medical Imaging.

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

[21]  Z. Tu,et al.  Automatic Segmentation of MS Lesions Using a Contextual Model for the MICCAI Grand Challenge , 2008, The MIDAS Journal.

[22]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[23]  Antonio Torralba,et al.  Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Antonio Torralba,et al.  Simultaneous detection and segmentation for generic objects , 2011, 2011 18th IEEE International Conference on Image Processing.

[25]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Xavier Lladó,et al.  Automated detection of multiple sclerosis lesions in serial brain MRI , 2012, Neuroradiology.

[27]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[28]  Wiro J. Niessen,et al.  White matter lesion extension to automatic brain tissue segmentation on MRI , 2009, NeuroImage.

[29]  Antonio Criminisi,et al.  TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context , 2007, International Journal of Computer Vision.

[30]  Antonio Cerasa,et al.  A Cellular Neural Network methodology for the automated segmentation of multiple sclerosis lesions , 2012, Journal of Neuroscience Methods.

[31]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.

[32]  A. Compston,et al.  Multiple sclerosis. , 2002, Lancet.

[33]  Xavier Lladó,et al.  A Supervised Approach for Multiple Sclerosis Lesion Segmentation Using Context Features and an Outlier Map , 2013, IbPRIA.

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

[35]  Jeffrey P. Sutton,et al.  Towards automated enhancement, segmentation and classification of digital brain images using networks of networks , 2001, Inf. Sci..

[36]  Xavier Lladó,et al.  Automatic microcalcification and cluster detection for digital and digitised mammograms , 2012, Knowl. Based Syst..

[37]  H Azhari,et al.  Automated detection and characterization of multiple sclerosis lesions in brain MR images. , 1998, Magnetic resonance imaging.

[38]  Xavier Lladó,et al.  A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies , 2014, Neuroradiology.

[39]  M Takahashi,et al.  Brain lesions: when should fluid-attenuated inversion-recovery sequences be used in MR evaluation? , 1999, Radiology.

[40]  Alex Rovira,et al.  MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI , 2014, Comput. Methods Programs Biomed..

[41]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[42]  D. Louis Collins,et al.  Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging , 2013, Medical Image Anal..