RESEARCH
29 weeks gestational age

Fetal Brain Atlas

The CRL has developed a mathematical framework for the generation of an unbiased, deformable spatiotemporal atlas of the fetal brain from magnetic resonance imaging (MRI) of normal fetuses scanned prenatally. Our atlas serves to capture the inter-subject anatomic variability of the fetal brain over the fetal brain growth period (Gholipour et al., 2017) and is currently available between 21 weeks gestational age to 38 weeks. The atlas has been constructed following an unbiased minimum distance template estimation approach which utilizes symmetric diffeomorphic deformation and the cross-correlation (CC) similarity metric integrated with kernel regression in age.

The main characteristic of our atlas construction method, as compared to other schemes such as those that have employed affine registration, Demons registration, or free-form deformation (FFD), is its sharpness, which indicates superior inter-subject registration performance achieved through symmetric diffeomorphic deformable registration. The atlas can thus be effectively used in spatial normalization (inter-subject image registration), fetal brain localization and matching, and atlas-based segmentation.

For these purposes, the atlas comes with brain tissue and structure labels including cortical gray matter, white matter, subcortical gray matter structures, CSF, lateral ventricles, brainstem, and cereblleum labels, and more. In addition, gestational age weeks 21-30 include labels for developing white matter layers, including the subplate, intermediate zone, and ventricular zone. An update of the atlas spanning 20-38 weeks gestational age, as well as a regionally divided cortex parcellation was released in February 2018. Future plans include expanding the atlas, again, to include earlier gestational ages. Please check back for updates.

Applications

Potential applications of the deformable spatiotemporal atlas of the fetal brain are the development of faster, more accurate, automated methods of MRI structural analysis, and disease biomarker identification in utero. Assessing the volume and overall morphology of the fetal ventricles, identifying and segmenting the transitory fetal subplate zone, and developing a method of automatic fetal MRI brain localization are some of the ongoing research projects.


Download the CRL fetal brain atlas

View the Atlas in 4D

Updated 02/05/2018

The paper reporting the updated atlas was published in Scientific Reports in 2017[1], which is open access. A link can be found in the reference below. The report of the original atlas construction was published in 2014[2].

Please contact us if there are any issues downloading the atlas images.


References

1. A Gholipour, CK Rollins, C Velasco-Annis, A Ouaalam, A Akhondi-Asl, O Afacan, C Ortinau, S Clancy, C Limperopoulos, E Yang, JA Estroff, and SK Warfield. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth, Scientific Reports 7, Article number: 476 (2017). [www]

2. A Gholipour, C Limperopoulos, S Clancy, C Clouchoux, A Akhondi-Asl, J A Estroff, and S K Warfield. Construction of a Deformable Spatiotemporal MRI Atlas of the Fetal Brain: Evaluation of Similarity Metrics and Deformation Models. MICCAI 2014. [PDF]


Other References

1. SSM Salehi, SR Hashemi, C Velasco-Annis, A Ouaalam, J Estroff, D Erdogmus, SK Warfield, and A Gholipour. Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning, IEEE International Symposium on Biomedical Imaging (ISBI 2018). [www]

2. SR Hashemi, SSM Salehi, D Erdogmus, S Prabhu, SK Warfield, and A Gholipour. Asymmetric Similarity Loss Function to Balance Precision and Recall in Highly Unbalanced Deep Medical Image Segmentation. IEEE Transactions on Medical Imaging (TMI 2018). [www]

3. B Marami, SSM Salehi, O Afacan, B Scherrer, C Rollins, E Yang, J Estroff, SK Warfield, and A Gholipour. Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. NeuroImage (2017). [www]