Abstract
We present a learning-based framework for automatic brain extraction in MR images. It accepts single or multi-contrast brain MR data, builds global binary random forests classifiers at multiple resolution levels, hierarchically performs voxelwise classifications for a test subject, and refines the brain surface using a narrow-band level set technique on the classification map. We further develop a data-driven schema to improve the model performance, which clusters patches of co-registered training images and learns cluster-specific classifiers. We validate our framework via experiments on single and multi-contrast datasets acquired using scanners with different magnetic field strengths. Compared to the state-of-the-art methods, it yields the best performance with statistically significant improvement of the cluster-specific method (with a Dice coefficient of 97.6 ± 0.4 % and an average surface distance of 0.8 ± 0.1 mm) over the global method.
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Notes
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We run BET, FS, FS+GCUT using default settings; for BET-R, we used bet with options -r -s. We did not include LABEL [16] due to its long testing time (7 min) for clinical practice.
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Liu, Y., Çetingül, H.E., Odry, B.L., Nadar, M.S. (2016). Learning Global and Cluster-Specific Classifiers for Robust Brain Extraction in MR Data. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_16
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