Atlas-based segmentation of brain magnetic resonance imaging using random walks

/in , , /by
Atlas-based segmentation of brain magnetic resonance imaging using random walks

Morin, Jean Philippe and Desrosiers, Christian and Duong, Luc

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2012

Abstract : The segmentation of brain magnetic resonance imaging is a difficult task, essential to several applications in neuroscience. Atlas-based methods are often employed for this task since they provide prior information in the form of labels, without the manual intervention of a trained technician. In this paper, we present a novel and efficient atlas-based segmentation method based on random walks. Unlike most atlas-based approaches, our method combines the registration and label propagation steps in a single efficient framework. Moreover, this method does not depend on a specific deformation model, making it more robust to complex transformations not captured by such models. Experiments on benchmark brain MRI data show the usefulness and efficiency of our method. © 2012 IEEE.

You might also like
Annealing based approach to optimize classification system
Unsupervised multi-target domain adaptation through knowledge distillation
Deep interpretable classification and weakly-supervised segmentation of histology images via max-min uncertainty
Content-based advertisement detection
Few-shot segmentation without meta-learning: A good transductive inference is all you need?
Exploring cortical folding pattern variability using local image features
Handwritten digits segmentation based on structural approach
Système hybride de reconnaissance de mots manuscrits sur un grand vocabulaire utilisant des réseaux neuronaux et des modèles de Markov cachés