Abstract
Fetal MRI is emerging as an effective, non-invasive tool in prenatal diagnosis and pregnancy follow-up. However, there is a significant variability of the position and orientation of the fetus in the MR images. This makes these images more difficult to analyze and interpret compared to standard adult MR imaging, which standardized anatomical imaging aligned planes. We address this issue by automatic localization of the fetal anatomy, in particular, the brain which is a structure of interest for many fetal MRI studies. We first extract superpixels followed by the computation of a histogram of features for each superpixel using bag of words based on dense scale invariant feature transform (DSIFT) descriptors. We construct a graph of superpixels and train a random forest classifier to distinguish between brain and non-brain superpixels. The localization framework has been tested on 55 MR datasets at gestational ages between 20–38 weeks. The proposed method was evaluated using 5-fold cross validation achieving a \(94.55\,\%\) brain detection accuracy rate.
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Acknowledgments
Thanks for the volunteer subjects and radiographers from St. Thomas Hospital London for the image acquisitions. We used the Medical Imaging Interaction Toolkit (MITK) [17] to visualize some of the figures. Amir Alansary is supported by the Imperial College PhD Scholarship.
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Alansary, A. et al. (2015). Automatic Brain Localization in Fetal MRI Using Superpixel Graphs. In: Bhatia, K., Lombaert, H. (eds) Machine Learning Meets Medical Imaging. MLMMI 2015. Lecture Notes in Computer Science(), vol 9487. Springer, Cham. https://doi.org/10.1007/978-3-319-27929-9_2
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DOI: https://doi.org/10.1007/978-3-319-27929-9_2
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