Automated Segmentation of the Craniofacial Skeleton With Black Bone Magnetic Resonance Imaging

Three-dimensional (3D) imaging of the craniofacial skeleton is integral in managing a wide range of bony pathologies. The authors have previously demonstrated the potential of Black Bone MRI (BB) as a non-ionizing alternative to CT. However, even in experienced hands 3D rendering of BB datasets can be challenging and time consuming. The objectives of this study were to develop and test a semi- and fully-automated segmentation algorithm for the craniofacial skeleton. Previously acquired adult volunteer (n = 15) BB datasets of the head were utilized. Imaging was initially 3D rendered with our conventional manual technique. An algorithm to remove the outer soft-tissue envelope was developed and 3D rendering completed with the processed datasets (semi-automated). Finally, a fully automated 3D-rendering method was developed and applied to the datasets. All 3D rendering was completed with Fovia High Definition Volume Rendering (Fovia Inc, Palo Alto, CA). Analysis was undertaken of the 3D visual results and the time taken for data processing and interactive manipulation. The mean time for manual segmentation was 12.8 minutes, 3.1 minutes for the semi-automated algorithm, and 0 minutes for the fully automated algorithm. Further fine adjustment was undertaken to enhance the automated segmentation results, taking a mean time of 1.4 minutes. Automated segmentation demonstrates considerable potential, offering significant time saving in the production of 3D BB imaging in adult volunteers. the authors continue to undertake further development of our segmentation algorithms to permit adaption to the pediatric population in whom non-ionizing imaging confers the most potential benefit.