Level Set Framework of Multi Labels Fusion for Multiple Sclerosis Lesion Segmentation

Multiple sclerosis (MS) lesion segmentation is important in estimating the progress of the disease and measuring the impact of new clinical treatments. In this paper, we present a multi-label fusion embedded level set method for White Matter (WM) lesion segmentation from Multiple Sclerosis (MS) patient images. Specifically we focus on the validation of the variational level set method. Lesion segmentation is achieved by extending the level set contour which consists of a label fusion term, an image data term and a regularization term. Labels are obtained from the fuzzy C-means model and embedded into the label fusion term. To compare the performance of our method with other state-of-the-art methods, we evaluated the methods with 20 MRI datasets of MS patients. Our approach exhibits a significantly higher accuracy on segmention of WM lesions over other evaluated methods.

[1]  T. Arbel,et al.  HIERARCHICAL MRF AND RANDOM FOREST SEGMENTATION OF MS LESIONS AND HEALTHY TISSUES IN BRAIN MRI , 2015 .

[2]  S.Sivagowri,et al.  AUTOMATIC LESION SEGMENTATION OFMULTIPLE SCLEROSIS IN MRI IMAGESUSING SUPERVISED CLASSIFIER , 2013 .

[3]  Christophe Collet,et al.  Multiple sclerosis lesion detection with local multimodal Markovian analysis and cellular automata ‘GrowCut’ , 2014 .

[4]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[5]  Saurabh Jain,et al.  Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images , 2015, NeuroImage: Clinical.

[6]  Christos Davatzikos,et al.  A Robust Energy Minimization Algorithm for MS-Lesion Segmentation , 2015, ISVC.

[7]  Alex Rovira,et al.  Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding , 2014, Comput. Methods Programs Biomed..

[8]  Simon K. Warfield,et al.  A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation , 2015, IEEE Transactions on Medical Imaging.

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

[10]  Bilwaj Gaonkar,et al.  Multi-atlas skull-stripping. , 2013, Academic radiology.

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

[12]  Shuanglu Dai,et al.  A Bregman divergence based Level Set Evolution for efficient medical image segmentation , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

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

[14]  Yu Liu,et al.  Level set framework of multi-atlas label fusion with applications to magnetic resonance imaging segmentation of brain region of interests and cardiac left ventricles , 2017 .

[15]  A. Oliver,et al.  A toolbox for multiple sclerosis lesion segmentation , 2015, Neuroradiology.

[16]  Lisa Tang,et al.  Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation , 2016, IEEE Transactions on Medical Imaging.

[17]  Paulo Guilherme de Lima Freire,et al.  Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images , 2016, Comput. Biol. Medicine.

[18]  Min Luo,et al.  A level set method for multiple sclerosis lesion segmentation. , 2017, Magnetic resonance imaging.