Brain extraction in partial volumes T2*@7T by using a quasi-anatomic segmentation with bias field correction

Publication date: 1 February 2018
Source:Journal of Neuroscience Methods, Volume 295
Author(s): João Valente, Pedro M. Vieira, Carlos Couto, Carlos S. Lima
BackgroundPoor brain extraction in Magnetic Resonance Imaging (MRI) has negative consequences in several types of brain post-extraction such as tissue segmentation and related statistical measures or pattern recognition algorithms. Current state of the art algorithms for brain extraction work on weighted T1 and T2, being not adequate for non-whole brain images such as the case of T2*FLASH@7T partial volumes.New methodThis paper proposes two new methods that work directly in T2*FLASH@7T partial volumes. The first is an improvement of the semi-automatic threshold-with-morphology approach adapted to incomplete volumes. The second method uses an improved version of a current implementation of the fuzzy c-means algorithm with bias correction for brain segmentation.ResultsUnder high inhomogeneity conditions the performance of the first method degrades, requiring user intervention which is unacceptable. The second method performed well for all volumes, being entirely automatic.Comparison with existing methodsState of the art algorithms for brain extraction are mainly semi-automatic, requiring a correct initialization by the user and knowledge of the software. These methods can't deal with partial volumes and/or need information from atlas which is not available in T2*FLASH@7T. Also, combined volumes suffer from manipulations such as re-sampling which deteriorates significantly voxel intensity structures making segmentation tasks difficult. The proposed method can overcome all these difficulties, reaching good results for brain extraction using only T2*FLASH@7T volumes.ConclusionsThe development of this work will lead to an improvement of automatic brain lesions segmentation in T2*FLASH@7T volumes, becoming more important when lesions such as cortical Multiple-Sclerosis need to be detected.



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