Brain extraction using the watershed transform from markers


Isolation of the brain from other tissue types in magnetic resonance (MR) images is an important step in many types of neuro-imaging research using both humans and animal subjects. The importance of brain extraction is well appreciated—numerous approaches have been published and the benefits of good extraction methods to subsequent processing are well known. We describe a tool—the marker based watershed scalper (MBWSS)—for isolating the brain in T1-weighted MR images built using filtering and segmentation components from the Insight Toolkit (ITK) framework. The key elements of MBWSS—the watershed transform from markers and aggressive filtering with large kernels—are techniques that have rarely been used in neuroimaging segmentation applications. MBWSS is able to reliably isolate the brain without expensive preprocessing steps, such as registration to an atlas, and is therefore useful as the first stage of processing pipelines. It is an informative example of the level of accuracy achievable without using priors in the form of atlases, shape models or libraries of examples. We validate the MBWSS using a publicly available dataset, a paediatric cohort, an adolescent cohort, intra-surgical scans and demonstrate flexibility of the approach by modifying the method to extract macaque brains.

Publication DOI:
Divisions: Life & Health Sciences
Additional Information: Copyright © 2013 Beare, Chen, Adamson, Silk, Thompson, Yang, Anderson, Seal and Wood.This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
PURE Output Type: Article
Published Date: 2013-12-09
Accepted Date: 2013-11-16
Submitted Date: 2013-08-27
Authors: Beare, Richard
Chen, Jian
Adamson, Christopher L.
Silk, Timothy
Thompson, Deanne K.
Yang, Joseph Y.M.
Anderson, Vicki A.
Seal, Marc L.
Wood, Amanda G. ( 0000-0002-1537-6858)



Version: Published Version

License: Creative Commons Attribution

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