We propose a fully automatic method for robust finger joint detection in T1 weighted magnetic resonance imaging (MRI) sequences for initialization of statistical shape model (SSM) based segmentation. We propose a robust method that only relies on few training samples. Therefore, a parallel-beam forward projection is calculated on the MRI volume. A trained Bagging classifier will detect the joints in 2D which are then splatted into the 3D volume. For evaluation, leave-one-out cross validation was performed. The detection of the joints in 2D yielded a Dice score of 0.67 ± 0.056 with respect to a manually obtained ground truth. For the initialization of SSM-based segmentation algorithms, the results are very promising.
CITATION STYLE
Bopp, J., Unberath, M., Steidl, S., Fahrig, R., Oliveira, I., Kleyer, A., & Maier, A. (2017). Automatic finger joint detection for volumetric hand imaging. In Informatik aktuell (pp. 104–109). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-662-49465-3_20
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