A multi-center milestone study of clinical vertebral CT segmentation. (April 2016) - Yao, Jianhua; Burns, Joseph E.; Forsberg, Daniel; Seitel, Alexander; Rasoulian, Abtin; Abolmaesumi, Purang; Hammernik, Kerstin; Urschler, Martin; Ibragimov, Bulat; Korez, Robert; Vrtovec, Tomaž; Castro-Mateos, Isaac; et al.
Highlights: The first multi-center milestone comparative study for vertebra segmentation methods. Objectively evaluate the performance of state-of-the-art vertebra segmentation algorithms. Construct a publicly available annotated reference data set for spine labeling and segmentation. Abstract: A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.