Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets

Quantitative analysis of cine cardiac magnetic resonance (CMR) images for the assessment of global left ventricular morphology and function remains a routine task in clinical cardiology practice. To date, this process requires user interaction and therefore prolongs the examination (i.e. cost) and introduces observer variability. In this study, we sought to validate the feasibility, accuracy, and time efficiency of a novel framework for automatic quantification of left ventricular global function in a clinical setting.

Measuring fund performance using multi-factor models: evidence for the Portuguese market

This paper examines the performance of Portuguese equity funds investing in the domestic and in the European Union market, using several unconditional and conditional multi-factor models. In terms of overall performance, we find that National funds are neutral performers, while European Union funds under-perform the market significantly. These results do not seem to be a consequence of management fees. Overall, our findings are supportive of the robustness of conditional multi-factor models. In fact, Portuguese equity funds seem to be relatively more exposed to smallcaps and more value-oriented. Also, they present strong evidence of time-varying betas and, in the case of the European Union funds, of time-varying alphas too. Finally, in terms of market timing, our tests suggest that mutual fund managers in our sample do not exhibit any market timing abilities. Nevertheless, we find some evidence of timevarying conditional market timing abilities but only at the individual fund level.

Semi-automatic 3D Segmentation Of Costal Cartilage In CT Data From Pectus Excavatum Patients

One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75±0.04 and an average mean surface distance of 1.69±0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.