Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression

  title={Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression},
  author={Ahmed M. Serag and Paul Aljabar and Gareth Ball and Serena J. Counsell and James P. Boardman and Mary A. Rutherford and Anthony David Edwards and Joseph V. Hajnal and Daniel Rueckert},

A spatio-temporal atlas of neonatal diffusion MRI based on kernel ridge regression

  • K. ShenJ. Fripp S. Rose
  • Medicine
    2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
  • 2017
A spatio-temporal atlas of diffusion MRI for neonatal brains between 32 to 42 weeks postmenstrual age (PMA) is developed based on Fibre Orientation Distribution (FOD) reconstruction of the diffusion data.

A Multi-channel 4D Probabilistic Atlas of the Developing Brain: Application to Fetuses and Neonates

This paper presents an approach for constructing a 4D multi-channel atlas of the developing preterm brain which incorporates multiple modalities and tissue segmentations and shows to improve atlas-based automatic segmentation comparing to probabilistic atlases generated using affine registration.

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth

An algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain is developed by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age and is available online as a reference for anatomy and for registration and segmentation.

Construction and validation of a database of head models for functional imaging of the neonatal brain

The construction of a database of individual structural priors of the neonatal head using 215 individual‐level datasets at ages 29–44 weeks postmenstrual age from the Developing Human Connectome Project is described and a method to segment the extra‐cerebral tissue against manual segmentation is validated.

Longitudinal diffeomorphic fetal brain atlas learning for tissue labeling using geodesic regression and graph cuts

This master’s thesis provides a novel longitudinal fetal brain atlas construction concept for geodesic image regression using three different age-ranges which are parametrized according to the developmental stage of the fetus.

A Longitudinal Diffeomorphic Atlas-Based Tissue Labeling Framework for Fetal Brains using Geodesic Regression

A novel longitudinal fetal brain atlas construction concept for geodesic image regression using three different ageranges which are parametrized according to the developmental stage of the fetus is provided.

Towards a 4D Spatio-Temporal Atlas of the Embryonic and Fetal Brain Using a Deep Learning Approach for Groupwise Image Registration

A deep learning approach is proposed for creation of a 4D spatio-temporal atlas of the embryonic and fetal brain using groupwise image registration using Voxelmorph for the creation of learned conditional atlases, which consists of an atlas generation and registration network.

Precise spatial normalization through novel neonatal brain MRI and CT atlas templates

This paper suggests a new approach for enhancing the normalization of CT scans to a reference space, and explains the steps towards creating MR and CT atlases, and demonstrates the created multimodal atlas.

Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration

An atlas which describes the dynamics of early development through mean images at weekly intervals and a continuous spatio-temporal deformation is constructed and the evolution of brain volumes calculated on preterm neonates is in agreement with recently published findings based on measures of cortical folding of fetuses at the equivalent age range.



A dynamic 4D probabilistic atlas of the developing brain

Unbiased diffeomorphic atlas construction for computational anatomy

Computational Anatomy to Assess Longitudinal Trajectory of Brain Growth

  • G. GerigB. Davis S. Joshi
  • Biology
    Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
  • 2006
The method is based on the previously published concept of unbiased atlas building, calculating the nonlinear average image of a population of images by simultaneous nonlinear deformable registration and the resulting center average image is sharp and encodes the average structure and geometry of the whole population.

A new framework for analyzing white matter maturation in early brain development

A new framework for analyzing early maturation in white matter is proposed that generates a normative spatiotemporal model and provides 3D maps of absolute and relative indices of maturation.

Growth patterns in the developing brain detected by using continuum mechanical tensor maps

The creation of spatially complex, four-dimensional quantitative maps of growth patterns in the developing human brain, detected using a tensor mapping strategy with greater spatial detail and sensitivity than previously obtainable is reported.

Construction of a patient-specific atlas of the brain: Application to normal aging

This paper proposes a method for the construction of patient-specific atlas for a given query subject from a large population cohort based on the similarity between the query subject and the subjects in the population cohort.

Template-O-Matic: A toolbox for creating customized pediatric templates