A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth
By A. Gholipour, C. Rollins, C. Velasco-Annis, Abdelhakim Ouaalam, A. Akhondi-Asl, O. Afacan, C. Ortinau, Sean Clancy, C. Limperopoulos, E. Yang and 2 more. Scientific Reports, 2017
339 citations recorded by the source corpus.
Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.
Subfields: Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/6b9497e31794efc98007967461d1946cfc18bb28
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In Pediatrics, Perinatology and Child Health
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, methodology): “This registration was performed through the procedure described in [41] and is briefly described here”