Kimerly A Powell1, Tanisha Kashikar2, Brad Hittle3, Don Stredney3, Thomas Kerwin3, Gregory J Wiet4. 1. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA. kimerly.powell@osumc.edu. 2. Ohio University Heritage College of Osteopathic, Ohio University, Athens, OH, USA. 3. Interface Laboratory, The Ohio State University, Columbus, OH, USA. 4. Department of Otolaryngology, Nationwide Children's Hospital and The Ohio State University, Columbus, OH, USA.
Abstract
PURPOSE: To develop a time-efficient automated segmentation approach that could identify surface structures on the temporal bone for use in surgical simulation software and preoperative surgical training. METHODS: An atlas-based segmentation approach was developed to segment the tegmen, sigmoid sulcus, exterior auditory canal, interior auditory canal, and posterior canal wall in normal temporal bone CT images. This approach was tested in images of 20 cadaver bones (10 left, 10 right). The results of the automated segmentation were compared to manual segmentation using quantitative metrics of similarity, Mahalanobis distance, average Hausdorff distance, and volume similarity. RESULTS: The Mahalanobis distance was less than 0.232 mm for all structures. The average Hausdorff distance was less than 0.464 mm for all structures except the posterior canal wall and external auditory canal for the right bones. Volume similarity was 0.80 or greater for all structures except the sigmoid sulcus that was 0.75 for both left and right bones. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. CONCLUSIONS: An atlas-based approach using a deformable registration of a Gaussian-smoothed temporal bone image and refinements using surface landmarks was successful in segmenting surface structures of temporal bone anatomy for use in pre-surgical planning and training.
PURPOSE: To develop a time-efficient automated segmentation approach that could identify surface structures on the temporal bone for use in surgical simulation software and preoperative surgical training. METHODS: An atlas-based segmentation approach was developed to segment the tegmen, sigmoid sulcus, exterior auditory canal, interior auditory canal, and posterior canal wall in normal temporal bone CT images. This approach was tested in images of 20 cadaver bones (10 left, 10 right). The results of the automated segmentation were compared to manual segmentation using quantitative metrics of similarity, Mahalanobis distance, average Hausdorff distance, and volume similarity. RESULTS: The Mahalanobis distance was less than 0.232 mm for all structures. The average Hausdorff distance was less than 0.464 mm for all structures except the posterior canal wall and external auditory canal for the right bones. Volume similarity was 0.80 or greater for all structures except the sigmoid sulcus that was 0.75 for both left and right bones. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. CONCLUSIONS: An atlas-based approach using a deformable registration of a Gaussian-smoothed temporal bone image and refinements using surface landmarks was successful in segmenting surface structures of temporal bone anatomy for use in pre-surgical planning and training.
Authors: Kimerly A Powell; Gregory J Wiet; Brad Hittle; Grace I Oswald; Jason P Keith; Don Stredney; Steven Arild Wuyts Andersen Journal: Int J Comput Assist Radiol Surg Date: 2021-02-13 Impact factor: 2.924