LesionBrain: An Online Tool for White Matter Lesion Segmentation

In this paper, we present a new tool for white matter lesion segmentation called lesionBrain. Our method is based on a 3-stage strategy including multimodal patch-based segmentation, patch-based regularization of probability map and patch-based error correction using an ensemble of shallow neural networks. Its robustness and accuracy have been evaluated on the MSSEG challenge 2016 datasets. During our validation, the performance obtained by lesionBrain was competitive compared to recent deep learning methods. Moreover, lesionBrain proposes automatic lesion categorization according to location. Finally, complementary information on gray matter atrophy is included in the generated report. LesionBrain follows a software as a service model in full open access.

[1]  Olivier Commowick,et al.  MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure , 2016, MICCAI 2016.

[2]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[3]  Pierrick Coupé,et al.  Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis , 2017, Human brain mapping.

[4]  D. Louis Collins,et al.  Multiple Sclerosis lesion segmentation using an automated multimodal Graph Cut , 2016 .

[5]  Tom Gundersen,et al.  Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation , 2016, BrainLes@MICCAI.

[6]  José V. Manjón,et al.  Improved estimates of partial volume coefficients from noisy brain MRI using spatial context , 2010, NeuroImage.

[7]  Pierrick Coupé,et al.  An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images , 2008, IEEE Transactions on Medical Imaging.

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

[9]  Pierrick Coupé,et al.  Author manuscript, published in "Journal of Magnetic Resonance Imaging 2010;31(1):192-203" DOI: 10.1002/jmri.22003 Adaptive Non-Local Means Denoising of MR Images with Spatially Varying Noise Levels , 2010 .

[10]  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..

[11]  Nikolaus Weiskopf,et al.  Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT) , 2011, NeuroImage.

[12]  D. Louis Collins,et al.  Nonlocal Intracranial Cavity Extraction , 2014, Int. J. Biomed. Imaging.

[13]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[14]  D. Louis Collins,et al.  BEaST: Brain extraction based on nonlocal segmentation technique , 2012, NeuroImage.

[15]  Pierrick Coupé,et al.  volBrain: An Online MRI Brain Volumetry System , 2015, Front. Neuroinform..

[16]  Hayit Greenspan,et al.  Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI , 2016, Int. J. Biomed. Imaging.

[17]  Pierrick Coupé,et al.  HIPS: A new hippocampus subfield segmentation method , 2017, NeuroImage.

[18]  Alex Rovira,et al.  Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach , 2017, NeuroImage.

[19]  D. Louis Collins,et al.  Rotation-invariant multi-contrast non-local means for MS lesion segmentation , 2015, NeuroImage: Clinical.

[20]  Pierrick Coupé,et al.  NABS: non-local automatic brain hemisphere segmentation. , 2015, Magnetic resonance imaging.

[21]  Simon K. Warfield,et al.  Tversky as a Loss Function for Highly Unbalanced Image Segmentation using 3D Fully Convolutional Deep Networks , 2018, ArXiv.

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

[23]  D. Louis Collins,et al.  Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.

[24]  Brian B. Avants,et al.  A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation , 2011, NeuroImage.

[25]  David H. Miller,et al.  Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria , 2017, The Lancet Neurology.