Abstract
Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the "success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.
Highlights
A prerequisite for many analytic approaches applied to magnetic resonance (MR) images of living subjects is the identification of the target organ on the image
This study describes a new atlas-based brain masking method, pincram, and provides strong evidence of its accuracy and robustness in a series of experiments mimicking real-world brain extraction tasks
Pincram is unique among library-based methods in that it processes any given T1-weighted target head image on the basis of data sets of labelled T1-weighted images acquired on other scanners
Summary
A prerequisite for many analytic approaches applied to magnetic resonance (MR) images of living subjects is the identification of the target organ on the image. When images are analysed visually, this usually happens implicitly, ie. Due to the exquisite pattern recognition capabilities of the human visual system, no specific treatment of the image is usually required to achieve this analytic separation. Contrariwise, automatic image analysis generally demands a mask that distinguishes the organ or region of interest from parts of the image that correspond to extraneous structures or to background. For structural 3D imaging of the human brain, especially MR imaging, a variety of brain extraction, skull stripping, or intracranial masking methods have been proposed. These can be distinguished by the level of expert involvement: PLOS ONE | DOI:10.1371/journal.pone.0129211. These can be distinguished by the level of expert involvement: PLOS ONE | DOI:10.1371/journal.pone.0129211 July 10, 2015
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