13 March 2017 Algorithmic evaluation of lower jawbone segmentations
Author Affiliations +
The lower jawbone (or mandible), is due to its exposure to complex biomechanical forces the largest and strongest facial bone in humans. In this publication, an algorithmic evaluation of lower jawbone segmentation with a cellular automata algorithm called GrowCut is presented. For an evaluation, the algorithmic segmentation results were compared with slice-by-slice segmentations from two specialized physicians, which is considered to assess the given ground truth. As a result, pure manual slice-by-slice outlining took on average 39 minutes (minimum 35 minutes and maximum 46 minutes). This stands in strong contrast to an algorithmic segmentation which needed only about one minute for an initialization, hence needing just a fraction of the manual contouring time. At the same time, the algorithmic segmentations could achieve an acceptable Dice Similarity Score (DSC) of nearly ninety percent when compared to the ground truth slice-by-slice segmentations generated by the physicians. This stands in direct comparison to somewhat above ninety percent Dice Score between the two manual segmentations of the jawbones. In summary, this contribution shows that an algorithmic GrowCut segmentation can be an alternative to the very time consuming manual slice-by-slice outlining in the clinical practice.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jan Egger, Jan Egger, Kerstin Hochegger, Kerstin Hochegger, Markus Gall, Markus Gall, Xiaojun Chen, Xiaojun Chen, Knut Reinbacher, Knut Reinbacher, Katja Schwenzer-Zimmerer, Katja Schwenzer-Zimmerer, Dieter Schmalstieg, Dieter Schmalstieg, Jürgen Wallner, Jürgen Wallner, } "Algorithmic evaluation of lower jawbone segmentations", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101370C (13 March 2017); doi: 10.1117/12.2249532; https://doi.org/10.1117/12.2249532

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