A multi-center milestone study of clinical vertebral CT 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.

Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge

“…However, direct and objective comparisons are difficult to achieve due to following two issues: 1) Different MR data sets acquired with different image acquisition protocols are used in different studies, and most of these MR data sets are not publicly available. Although there exists one open data set for comparing algorithms for clinical vertebral segmentation from 3D CT data (Yao et al, 2016), to the best of our knowledge, there exists only one open MR data set with the associated manual delineation of IVDs (Oktay and A C C E P T E D M A N U S C R I P T Akgul, 2013). This open MR data set, however, cannot be used to evaluate and compare 3D IVD localization and segmentation algorithms, as only 2D mid-sagittal slices are available in the data set; and 2) Different evaluation metrics are used in different studies, which precludes the possibility of direct comparison.…”

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

“…(41,(73)(74)(75) Data sets such as these are often used for competitive challenges typically in association with an image processing conference. Examples of available data sets include those for bone age assessment, knee MRI, bone radiographs, and spine imaging.…”

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

“…However, many of these works either do not segment all thoracic and lumbar vertebrae, or they are not completely automatic, or have been only tested in healthy cases . The presence of the ribs is one of the problems for which different methods show less accuracy in the segmentation of the thoracic region . This is mainly due to the difficulty of discriminating between these structures and the vertebrae.…”