Automated Segmentation of the Sigmoid Sinus using a Multi-Atlas Approach

Abstract

To develop an accurate, automated multi-atlas segmentation algorithm for creating three-dimensional sigmoid sinus models from clinical computed tomography (CT) volumes for use in temporal bone mastoidectomy surgical simulation software.Methods: Clinical CT and micro-CT scans of 38 cadaveric temporal bones were used to develop and validate the algorithm. A single-atlas and multi-atlas segmentation were compared for accuracy using three different label fusion methods: majority voting, STAPLE, and joint label fusion. The automated segmentation algorithm was evaluated by comparing to ground truth manual segmentations through a combination of visual inspection and Dice, Hausdorff distance, and average Hausdorff distance metrics. Results:The best results were obtained for multi-atlas segmentation using joint label fusion for which a mean Dice value of 0.77 was found across all samples when compared to the manual segmentations. The mean Hausdorff distance was 10.39 mm, and the mean average Hausdorff distance was 0.30 mm, corresponding to less than two voxels. Visual inspection revealed accurate and high-resolution segmentations. Conclusion:The presented multi-atlas method is effective and accurate at automatically producing high-resolution segmentations of the sigmoid sinus for the purpose of surgical simulation.