Meeting Banner
Abstract #0813

GIF_boost : A Generalisable Hybrid Brain Tissue Segmentation with DeepLearning

JIAMING WU1, Giuseppe Pontillo1,2, Zoe Mendelsohn1,2,3, Yipeng Hu1, Frederik Barkhof1,4, and Ferran Prados1,2,5
1Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom, 2Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 3Department of Radiology, Charité School of Medicine and University Hospital Berlin, Berlin, Germany, 4Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands, 5e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain

Synopsis

Keywords: Segmentation, Segmentation

Image segmentation and parcellation can provide quantitative assessment of the brain and can guide diagnosis and treatment decision-making. Geodesic Information Flow (GIF) is a freely available brain tissue segmentation and parcellation MRI-based tool using a classical label fusion approach. In this work, we introduce GIF_boost, a hybrid solution that takes advantages of deep learning to accelerate the bottleneck step of the template library registration. We compared GIF_boost with the original version of GIF and FreeSurfer (a state-of-the-art method). GIF_boost performed parcellation minimum 16 times faster. Parcellations had a similar Dice coefficient and Hausdorff distance and an improved volumetric quantification.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords