Accuracy of bone segmentation and surface generation strategies analyzed by using synthetic CT volumes

Different kinds of bone measurements are commonly derived from computed-tomography (CT) volumes to answer a multitude of questions in biology and related fields. The underlying steps of bone segmentation and, optionally, polygon surface generation are crucial to keep the measurement error small. In this study, the performance of different, easily accessible segmentation techniques (global thresholding, automatic local thresholding, weighted random walk, neural network, and watershed) and surface generation approaches (different algorithms combined with varying degrees of simplification) was analyzed and recommendations for minimizing inaccuracies were derived. The different approaches were applied to synthetic CT volumes for which the correct segmentation and surface geometry were known. The most accurate segmentations of the synthetic volumes were achieved by setting a case-specific window to the gray value histogram and subsequently applying automatic local thresholding with appropriately chosen thresholding method and radius. Surfaces generated by the Amira® module Generate Lego Surface in combination with careful surface simplification were the most accurate. Surfaces with sub-voxel accuracy were obtained even for synthetic CT volumes with low contrast-to-noise ratios. Segmentation trials with real CT volumes supported the findings. Very accurate segmentations and surfaces can be derived from CT volumes by using readily accessible software packages. The presented results and derived recommendations will help to reduce the measurement error in future studies. Furthermore, the demonstrated strategies for assessing segmentation and surface qualities can be adopted to quantify the performance of new segmentation approaches in future studies.