Int J Comput Assist Radiol Surg - Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

Tópicos

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Resumo

RPOSE: Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested.METHODS: Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain.RESULTS: The system was evaluated with 20?GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012).CONCLUSION: A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Resumo Limpo

rpose detect segment brain tumor glioblastoma multiform gbm magnet reson mr imag often challeng due intrins heterogen signal characterist robust segment method brain tumor mri scan develop testedmethod simpl threshold statist method unabl adequ segment various element gbm local contrast enhanc necrosi edema voxelbas method achiev satisfactori result larger data set method base generat discrimin model intrins limit applic small sampl set learn transfer new method develop overcom challeng multimod mr imag segment superpixel use algorithm allevi sampl issu improv sampl repres next featur extract superpixel use multilevel gabor wavelet filter base featur support vector machin svm model affin metric model tumor train overcom limit previous generat model base output svm spatial affin model condit random field theori appli segment tumor maximum posteriori fashion given smooth prior defin affin model final label nois remov use structur knowledg symmetr continu characterist tumor spatial domainresult system evalu gbm case brat challeng data set dice coeffici comput result high consist report zikic et al miccai lectur note comput scienc vol pp conclus brain tumor segment method use modelawar affin demonstr compar perform stateofth art algorithm

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