Design of SOA services: experiences from industry

Because of the unknown usage scenarios, designing the elementary services of a service-oriented architecture (SOA), which form the basis for later composition, is rather difficult. Various design guide lines have been proposed by academia, tool vendors and consulting companies, but they differ in the rigor of validation and are often biased toward some technology. For that reason a multiple-case study was conducted in five large organizations that successfully introduced SOA in their daily business. The observed approaches are contrasted with the findings from a literature review to derive some recommendations for SOA service design.

Multi-modal glioblastoma segmentation: man versus machine

BACKGROUND AND PURPOSE Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. METHODS We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. RESULTS Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. CONCLUSIONS In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

A new universal, standardized implant database for product identification: a unique tool for arthroplasty registries.

INTRODUCTION Every joint registry aims to improve patient care by identifying implants that have an inferior performance. For this reason, each registry records the implant name that has been used in the individual patient. In most registries, a paper-based approach has been utilized for this purpose. However, in addition to being time-consuming, this approach does not account for the fact that failure patterns are not necessarily implant specific but can be associated with design features that are used in a number of implants. Therefore, we aimed to develop and evaluate an implant product library that allows both time saving barcode scanning on site in the hospital for the registration of the implant components and a detailed description of implant specifications. MATERIALS AND METHODS A task force consisting of representatives of the German Arthroplasty Registry, industry, and computer specialists agreed on a solution that allows barcode scanning of implant components and that also uses a detailed standardized classification describing arthroplasty components. The manufacturers classified all their components that are sold in Germany according to this classification. The implant database was analyzed regarding the completeness of components by algorithms and real-time data. RESULTS The implant library could be set up successfully. At this point, the implant database includes more than 38,000 items, of which all were classified by the manufacturers according to the predefined scheme. Using patient data from the German Arthroplasty Registry, several errors in the database were detected, all of which were corrected by the respective implant manufacturers. CONCLUSIONS The implant library that was developed for the German Arthroplasty Registry allows not only on-site barcode scanning for the registration of the implant components but also its classification tree allows a sophisticated analysis regarding implant characteristics, regardless of brand or manufacturer. The database is maintained by the implant manufacturers, thereby allowing registries to focus their resources on other areas of research. The database might represent a possible global model, which might encourage harmonization between joint replacement registries enabling comparisons between joint replacement registries.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

The Application of Digital Rights Management Systems in the Music Industry - An Empirical Investigation

Technology advances in hardware, software and IP-networks such as the Internet or peer-to-peer file sharing systems are threatening the music business. The result has been an increasing amount of illegal copies available on-line as well as off-line. With the emergence of digital rights management systems (DRMS), the music industry seems to have found the appropriate tool to simultaneously fight piracy and to monetize their assets. Although these systems are very powerful and include multiple technologies to prevent piracy, it is as of yet unknown to what extent such systems are currently being used by content providers. We provide empirical analyses, results, and conclusions related to digital rights management systems and the protection of digital content in the music industry. It shows that most content providers are protecting their digital content through a variety of technologies such as passwords or encryption. However, each protection technology has its own specific goal, and not all prevent piracy. The majority of the respondents are satisfied with their current protection but want to reinforce it for the future, due to fear of increasing piracy. Surprisingly, although encryption is seen as the core DRM technology, only few companies are currently using it. Finally, half of the respondents do not believe in the success of DRMS and their ability to reduce piracy.