Kainz B, Keraudren K, Kyriakopoulou V, Rutherford M, Hajnal JV, Rueckert D (2014)
Publication Type: Conference contribution
Publication year: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 1230-1233
Conference Proceedings Title: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Event location: Beijing, CHN
ISBN: 9781467319591
DOI: 10.1109/isbi.2014.6868098
Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90.
APA:
Kainz, B., Keraudren, K., Kyriakopoulou, V., Rutherford, M., Hajnal, J.V., & Rueckert, D. (2014). Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 1230-1233). Beijing, CHN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Kainz, Bernhard, et al. "Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors." Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, CHN Institute of Electrical and Electronics Engineers Inc., 2014. 1230-1233.
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