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

• Published in: Journal of neuroscience methods Vol. 295; pp. 129 - 138
• Main Authors: Valente, João, Vieira, Pedro M., Couto, Carlos, Lima, Carlos S.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2018
• Subjects: Algorithms; Brain - diagnostic imaging; Brain extraction; Fuzzy Logic; Head - diagnostic imaging; High resolution MRI; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Movement; Multiple sclerosis; Partial brain scanning; Pattern Recognition, Automated - methods; Software; Multiple sclerosis; Partial brain scanning; High resolution MRI; Brain extraction
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2017.12.006

•The paper analyses the performance of several current methods for brain extraction.•Current methods for T1 and T2 MRI doesn’t work correctly in T2*FLASH@7T sequences.•Automation of the threshold-with-morphology method was achieved using fuzzy c-means.•The new method achieves good results with partial volumes of T2*@7T. Poor 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. This 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. Under 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. State 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. The 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.