A multi-center milestone study of clinical vertebral CT segmentation

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

Diagonal cumulation of origin as an institutional incentive mechanism for cost optimisation in contemporary international business

In the emerging process of market globalisation, firms’ strategic options for optimising their cross-border operations have broadened. This paper presents a strategic framework for developing an appropriate strategic response of firms by relocating their production facilities and framing their operational procedures in such a way that they may, as eligible beneficiaries, exploit institutional incentive mechanisms available in a specific region or a host country. More specifically, in the paper we explore a strategic option for lowering firms’ operational costs through international operations by using the mechanism of diagonal cumulation of origin introduced by the European Union (EU) in its Common Commercial Policy towards selected non-member countries. Despite extensive discussions in theoretical literature on the conception of the rules of origin, only a few studies have explored the implications of this mechanism from the perspective of a firm’s transaction costs in international business. This paper shows that the ‘SAP+ diagonal cumulation of origin,’ when properly conceived and implemented by a firm, can positively affect its financial performance. By analysing the cost-effect simulation of a selected household appliance producer in the presented case study, we then discuss the key strategic and operational implications for firms wishing to take advantage of offered supranational institutional incentive mechanisms

Learning a Statistical Full Spine Model from Partial Observations

International audienceThe study of the morphology of the human spine has attracted research attention for its many potential applications, such as image segmentation, bio-mechanics or pathology detection. However, as of today there is no publicly available statistical model of the 3D surface of the full spine. This is mainly due to the lack of openly available 3D data where the full spine is imaged and segmented. In this paper we propose to learn a statistical surface model of the full-spine (7 cervical, 12 thoracic and 5 lumbar vertebrae) from partial and incomplete views of the spine. In order to deal with the partial observations we use probabilistic principal component analysis (PPCA) to learn a surface shape model of the full spine. Quantitative evaluation demonstrates that the obtained model faithfully captures the shape of the population in a low dimensional space and generalizes to left out data. Furthermore, we show that the model faithfully captures the global correlations among the vertebrae shape. Given a partial observation of the spine, i.e. a few vertebrae, the model can predict the shape of unseen vertebrae with a mean error under 3 mm. The full-spine statistical model is trained on the VerSe 2019 public dataset and is publicly made available to the community for non-commercial purposes