A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling

In this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tumors by using multi-atlas patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model automatically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing.

[1]  Mert R. Sabuncu,et al.  Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..

[2]  Christopher M. Bishop,et al.  Robust Bayesian Mixture Modelling , 2005, ESANN.

[3]  Daniel Rueckert,et al.  A Probabilistic Patch-Based Label Fusion Model for Multi-Atlas Segmentation With Registration Refinement: Application to Cardiac MR Images , 2013, IEEE Transactions on Medical Imaging.

[4]  David G. Lowe,et al.  Scalable Nearest Neighbor Algorithms for High Dimensional Data , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  Jennifer L. Cuzzocreo,et al.  Segmentation of Brain Images Using Adaptive Atlases with Application to Ventriculomegaly , 2011, IPMI.

[7]  Sébastien Ourselin,et al.  Template-Based Multimodal Joint Generative Model of Brain Data , 2015, IPMI.

[8]  Torsten Rohlfing,et al.  Extraction and Application of Expert Priors to Combine Multiple Segmentations of Human Brain Tissue , 2003, MICCAI.

[9]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[10]  Laurent Capelle,et al.  Preferential brain locations of low‐grade gliomas , 2004, Cancer.

[11]  P. Golland,et al.  On the Importance of Location and Features for the Patch-Based Segmentation of Parotid Glands , 2014, The MIDAS Journal.

[12]  Olivier Clatz,et al.  Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications , 2007 .

[13]  E. Mandonnet,et al.  The importance of measuring the velocity of diameter expansion on MRI in upfront management of suspected WHO grade II glioma - case report. , 2013, Neuro-Chirurgie.

[14]  Anssi Auvinen,et al.  Incidence of gliomas by anatomic location. , 2007, Neuro-oncology.

[15]  Ben Glocker,et al.  Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR , 2012, MICCAI.

[16]  Susan M. Chang,et al.  Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  D. Rubin,et al.  ML ESTIMATION OF THE t DISTRIBUTION USING EM AND ITS EXTENSIONS, ECM AND ECME , 1999 .

[18]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[19]  Ben Glocker,et al.  Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization , 2013, MICCAI.

[20]  Bennett A. Landman,et al.  Out-of-atlas labeling: A multi-atlas approach to cancer segmentation , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[21]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[22]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[23]  Brian B. Avants,et al.  Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR , 2014, Neuroinformatics.

[24]  Christian Wachinger,et al.  Atlas-Based Under-Segmentation , 2014, MICCAI.

[25]  Christos Davatzikos,et al.  Combining Generative Models for Multifocal Glioma Segmentation and Registration , 2014, MICCAI.

[26]  Sébastien Ourselin,et al.  STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation , 2013, Medical Image Anal..

[27]  Nicholas Ayache,et al.  A Generative Model for Brain Tumor Segmentation in Multi-Modal Images , 2010, MICCAI.

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

[29]  Mert R. Sabuncu,et al.  A Generative Model for Image Segmentation Based on Label Fusion , 2010, IEEE Transactions on Medical Imaging.

[30]  Nikos Paragios,et al.  Graph Based Spatial Position Mapping of Low-grade Gliomas , 2013 .

[31]  Daniel Rueckert,et al.  Automatic anatomical brain MRI segmentation combining label propagation and decision fusion , 2006, NeuroImage.

[32]  Colin Studholme,et al.  A Supervised Patch-Based Approach for Human Brain Labeling , 2011, IEEE Transactions on Medical Imaging.

[33]  Bjoern H Menze,et al.  Patch-based Segmentation of Brain Tissues , 2013 .

[34]  M. Okada,et al.  [New response evaluation criteria in solid tumours-revised RECIST guideline (version 1.1)]. , 2009, Gan to kagaku ryoho. Cancer & chemotherapy.