Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases

Marie Cherel; Francois Budin; Marcel Prastawa; Guido Gerig; Kevin Lee; Claudia Buss; Amanda Lyall; Kirsten Zaldarriaga Consing; Martin Styner

doi:10.1117/12.2082209

20 March 2015 Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases

Marie Cherel, Francois Budin, Marcel Prastawa, Guido Gerig, Kevin Lee, Claudia Buss, Amanda Lyall, Kirsten Zaldarriaga Consing, Martin Styner

Author Affiliations +

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 941311 (2015) https://doi.org/10.1117/12.2082209
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule.

Citation Download Citation

Marie Cherel, Francois Budin, Marcel Prastawa, Guido Gerig, Kevin Lee, Claudia Buss, Amanda Lyall, Kirsten Zaldarriaga Consing, and Martin Styner "Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941311 (20 March 2015); https://doi.org/10.1117/12.2082209

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available