Abstract
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.
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Bopp, J. et al. (2016). Automatic Finger Joint Detection for Volumetric Hand Imaging. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2016. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49465-3_20
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DOI: https://doi.org/10.1007/978-3-662-49465-3_20
Publisher Name: Springer Vieweg, Berlin, Heidelberg
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