Abstract
We propose an atlas-based segmentation framework for brain magnetic resonance images, specially designed to fit neonatal images, which pose additional difficulties due to the poor differentiation between the gray and white matter. The main contribution of our work consists of a gray matter enhancing step, which is applied to either the T1w or T2w modalities after standard preprocessing and alignment steps are carried out. Our enhancing step uses Hessian and box filters for the cortical gray matter and takes advantage of both local and non-local information for the subcortical gray matter. We consider four classes, and our framework has been evaluated using publicly available data from the NeoBrainS12 challenge.
Similar content being viewed by others
References
Adhikari, S.K., Sing, J.K., Basu, D.K., Nasipuri, M., Saha, P.K.: A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces. Signal Image Video Process. 9(8), 1945–1954 (2015)
Avants, B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)
Awate, S., Yushkevich, P., Song, Z., Licht, D., Gee, J.: Cerebral cortical folding analysis with multivariate modeling and testing: studies on gender differences and neonatal development. NeuroImage 53(2), 450–459 (2010)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A., Klein, A., Gee, J.C.: Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 130–137 (1998)
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., Contributors, D.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014)
Gui, L., Lisowski, R., Faundez, T., Höppi, P.S., Lazeyras, F., Kocher, M.: Morphology-driven automatic segmentation of MR images of the neonatal brain. Med. Image Anal. 16(8), 1565–1579 (2012)
Išgum, I., Benders, M.J.N.L., Avants, B., Cardoso, M.J., Counsell, S.J., Fischi Gomez, E., Gui, L., Hüppi, P.S., Kersbergen, K.J., Makropoulos, A., Melbourne, A., Moeskops, P., Mol, C.P., Kuklisova-Murgasova, M., Rueckert, D., Schnabel, J.A., Srhoj-Egekher, V., Wu, J., Wang, S., de Vries, L.S., Viergever, M.A.: Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge. Med. Image Anal. 20(1), 135–151 (2015)
Makropoulos, A.: Automatic MRI segmentation of the developing neonatal brain. Doctoral thesis, Imperial College London (2014)
Mukherjee, P.S.: A multi-resolution and adaptive 3-D image denoising framework with applications in medical imaging. Signal Image Video Process. 11, 1379–1387 (2017)
NeoBrainS12: MICCAI Grand Challenge on Neonatal Brain Segmentation (2012). http://neobrains12.isi.uu.nl
Ocegueda, O., Dalmau, O., Garyfallidis, E., Descoteaux, M., Rivera, M.: On the computation of integrals over fixed-size rectangles of arbitrary dimension. Pattern Recognit. Lett. 79, 68–72 (2016)
Roselli, M., Matute, E., Ardila, A.: Neuropsicología del desarrollo infantil. El Manual Moderno S.A. de C.V. (2010)
Shi, F., Fan, Y., Tang, S., Gilmore, J.H., Lin, W., Shen, D.: Neonatal brain image segmentation in longitudinal mri studies. NeuroImage 49(1), 391–400 (2010)
Shi, F., Yap, P.T., Fan, Y., Cheng, J.Z., Wald, L.L., Gerig, G., Lin, W., Shen, D.: Cortical enhanced tissue segmentation of neonatal brain MR images acquired by a dedicated phased array coil. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009, pp. 39–45. IEEE (2009)
Song, Z., Awate, S., Licht, D., Gee, J.: Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based Markov priors. In: MICCAI, vol. 10, pp. 883–890 (2007)
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D.: Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 89, 152–164 (2014)
Wang, L., Shi, F., Li, G., Gao, Y., Lin, W., Gilmore, J., Shen, D.: Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 84, 141–158 (2014)
Wu, J., Avants, B.: Automatic registration-based segmentation for neonatal brains using ANTs and atropos. In: MICCAI Grand Challenge on Neonatal Brain Segmentation 2012 (NeoBrainS12), pp. 36–47 (2012)
Acknowledgements
This work was supported in part by CONACYT (Mexico), Grant 258033.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rodríguez-Domínguez, U., Dalmau, O., Ocegueda, O. et al. Atlas-based segmentation of neonatal brain MR images using a gray matter enhancing step. SIViP 12, 633–640 (2018). https://doi.org/10.1007/s11760-017-1202-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-017-1202-8