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In clinical scenarios, there is an increasing interest in complex computational experiments, as for example the training of Deep Learning models. Reproducibility is an essential property of such experiments, especially if the result contributes to a patient’s treatment. This paper introduces Curious Containers, a software framework for computational reproducibility that treats data, software and runtime environment as decentralized network resources. All experiment resources are described in a single file, using a new format that is compatible with a subset of the Common Workflow Language. Docker is used to deploy the experiment software in a container image, including arbitrary data transmission programs to connect with existing storage solutions. The framework supports Deep Learning applications, that have a high demand in storage and processing capabilities. Large datasets can be mounted inside containers via network filesystems like SSHFS based on the filesystem in user-space technology. The Nvidia-Container-Toolkit enables GPU usage. Curious Containers has been tested in two biomedical scenarios. The first use case is a Deep Learning application for tumor classification in images that requires a large dataset and a GPU. In this context, a prototypical integration of the framework with the existing Data Version Control system for exploratory Deep Learning modeling has been developed. The second use case extends an existing container image, including a scientific workflow for detection and comparison of human protein in mass spectrography data. The container image was originally developed for an archiving platform and could be extended to be compatible with both Curious Containers and cwltool, the Common Workflow Language reference implementation. The presented solution allows for consistent description and execution of computational experiments, while trying to be both flexible and interoperable with existing software and standards. Support for Deep Learning experiments is gaining importance as such systems are increasingly validated as medical decision support systems.

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges. This special issue contains papers showing existing solutions and latest developments in Life Sciences and Computing Sciences to collaboratively explore new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making.

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS. We would like to thank the sponsors of the MS Data Alliance. The MSDA receives income from a range of corporate sponsors, recently including: Biogen, BristolMyersSquibb (formerly Celgene), Canopy Growth Corporation, Genzyme, Icometrix, Janssen, Merck, Mylan, Novartis, QMENTA, Quanterix, Roche. We would like to thank the European MS Platform and the University MS Center of Hasselt University for founding the initiative. Next to this, we would like to thank the other core partners for their continued support: the European Charcot Foundation, The European Institute for Innovation through Health Data, the University of Gottingen and KULeuven. A special thanks to our pioneer registries: the UK MS registry and the German MS registry. Peeters, LM (corresponding author), Agoralaan C Bis, B-3590 Diepenbeek, Belgium. Liesbet.peeters@uhasselt.be

Convolutional Neural Networks (CNN) are used for automatic cancer detection in pathological images. These data-driven experiments are difficult to reproduce, because the CNNs may require CUDA-enabled Nvidia GPUs for acceleration and training is often performed on a large dataset stored on a researcher's computer, inaccessible to others. We introduce the RED file format for reproducible experiment description, where executable programs are packaged and referenced as Docker container images. Data inputs and outputs are described as network resources using standard transmission and authentication protocols instead of local file paths. Following the FAIR guiding principles, the RED format is based on and compatible with the established Common Workflow Language specification. RED files are interpreted by the accompanying Curious Containers (CC) software. Arbitrarily large datasets are mounted inside containers via FUSE network filesystems like SSHFS. SSHFS is compared to NFS and a local SSD in artificial benchmarks and in the context of a CNN training scenario, where SSHFS introduces a performance decrease by a factor of 1.8. We are convinced that RED can greatly improve the reproducibility of deep learning workloads and data-driven experiments. This is in particular important in clinical scenarios where the result of an analysis may contribute to a patient's treatment.

This paper describes the teaching and learning experiences made at the women's Bachelor degree programme ‘Computer science and business administration’. It focuses on the consistent orientation towards practical experiences. Different courses are described showing the strong emphasis on practical training. One special course is described in more detail: ‘Industrial project’, which is held in both semesters, third and fifth, and where teams of students perform real projects for private clients from industry. It is shown that the entire programme is geared to offer practical experiences as much as possible to the students. After two semesters of teaching foundations of computer science, the students carry out the first industrial project supported jointly with advanced students from the fifth semester. At the fourth semester the students complete their 4-5 months internship, the fifth semester students attend the industrial project again and in the sixth semester the students write their Bachelor thesis, usually at an industrial partner's site. The women-only Bachelor degree programme has started in 2009 and has been developed to a success story since then. Almost all graduates have found a job immediately after the studies, or have continued a subsequent Master programme.

Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia.

Replication of computational experiments is essential for verifiable research. However, it requires a comprehensive and unambiguous description of all employed digital artifacts, in particular data, code and the computational environment. Recently, the FAIR Guiding Principles have been published to support reproducible research. In this paper, a cloud-based biomedical collaboration platform has been evaluated regarding FAIR principles and has been extended to support reproducibility. The FAICE suite is presented, encompassing tools to thoroughly describe and reproduce a computational experiment within the original execution environment as well as within a dynamically configured VM.

Applications for data analysis of biomedical data are complex programs and often consist of multiple components. Re-usage of existing solutions from external code repositories or program libraries is common in algorithm development. To ease reproducibility as well as transfer of algorithms and required components into distributed infrastructures Linux containers are increasingly used in those environments, that are at least partly connected to the internet. However concerns about the untrusted application remain and are of high interest when medical data is processed. Additionally, the portability of the containers needs to be ensured by using only security technologies, that do not require additional kernel modules. In this paper we describe measures and a solution to secure the execution of an example biomedical application for normalization of multidimensional biosignal recordings. This application, the required runtime environment and the security mechanisms are installed in a Docker-based container. A fine-grained restricted environment (sandbox) for the execution of the application and the prevention of unwanted behaviour is created inside the container. The sandbox is based on the filtering of system calls, as they are required to interact with the operating system to access potentially restricted resources e.g. the filesystem or network. Due to the low-level character of system calls, the creation of an adequate rule set for the sandbox is challenging. Therefore the presented solution includes a monitoring component to collect required data for defining the rules for the application sandbox. Performance evaluation of the application execution shows no significant impact of the resulting sandbox, while detailed monitoring may increase runtime up to over 420%.

Introduction We investigate how characteristics of sleep-wake dynamics in humans are modified by narcolepsy, a clinical condition that is supposed to destabilize sleep-wake regulation. Subjects with and without cataplexy are considered separately. Differences in sleep scoring habits as a possible confounder have been examined. Aims and methods Four groups of subjects are considered: narcolepsy patients from China with (n = 88) and without (n = 15) cataplexy, healthy controls from China (n = 110) and from Europe (n = 187, 2 nights each). After sleep-stage scoring and calculation of sleep characteristic parameters, the distributions of wake-episode durations and sleep-episode durations are determined for each group and fitted by power laws (exponent α) and by exponentials (decay time τ). Results We find that wake duration distributions are consistent with power laws for healthy subjects (China: α = 0.88, Europe: α = 1.02). Wake durations in all groups of narcolepsy patients, however, follow the exponential law (τ = 6.2-8.1 min). All sleep duration distributions are best fitted by exponentials on long time scales (τ = 34-82 min). Conclusions We conclude that narcolepsy mainly alters the control of wake-episode durations but not sleep-episode durations, irrespective of cataplexy. Observed distributions of shortest wake and sleep durations suggest that differences in scoring habits regarding the scoring of short-term sleep stages may notably influence the fitting parameters but do not affect the main conclusion.

Objective Physiological networks (PN) model couplings between organs in a high-dimensional parameter space. Machine learning methods, in particular artifical neural networks (ANNs), are powerful on high-dimensional classification tasks. However, lack of interpretability of the resulting models has been a drawback in research. We assess relevant PN topology changes in obstructive sleep apnea (OSA) by novel ANN interpretation techniques. Approach ANNs are trained to classify OSA based on the PNs of 48 patients and 48 age and gender matched healthy controls. The PNs consisting of 2812 links are derived from overnight biosignal recordings. The interpretation technique DeepLift is applied to the resulting ANN models, enabling the determination of the relevant features for classification decisions on individual subjects. The mean relevance scores of the features are compared to other machine learning methods (decision tree and random forests) and statistical tests on group differences. Main results The ANN interpretation results show good agreement with the compared methods and 87% of the samples could be correctly classified. OSA patients show a significantly higher coupling (p [Formula: see text] 0.001) in light sleep (N2) between breathing rate and EEG [Formula: see text] power in all electrode locations and to chin and leg muscular tone. In deep sleep (N3), OSA leads to significantly lower coupling (p [Formula: see text] 0.01) in lateral connections of EEG [Formula: see text] and [Formula: see text] power in central and frontal positions. Misclassified OSA patients had all mild/moderate AHIs and did not show PN topology changes. Both nights of these patients have been consistently misclassified as healthy. This may indicate, that the impact of respiratory events differs in subjects, thus forming different phenotypes. Significance The proposed PN analysis provides a powerful and robust method to quantify a broad range of physiological interactions. Interpretability of the ANN make them a promising tool to identify new diagnostic markers in data-driven approaches.

Biomedical analysis, in particular image and biosignal analysis, often requires several methods applied to the same data. The data is typically of large volume, so data transfer can become a bottleneck in remote analysis. Furthermore, biomedical data may contain patient data, raising data protection issues. We propose a highly virtualized infrastructure, employing Docker Swarm technology as the computing infrastructure. An underlying Openstack based IaaS cloud provides additional security features for a flexible and efficient multi-tenant analysis platform. We introduce the prototype infrastructure along a sample use-case of multiple versions of a machine-learning method applied to feature sets extracted from multidimensional biosignal recordings from Sleep Apnea patients and healthy controls.

Grid computing, the collaboration of distributed resources across institutional borders, is an emerging technology to meet the rising demand on computing power and storage capacity in fields such as high-energy physics, climate modeling, or more recently, life sciences. A secure, reliable, and highly efficient data transport plays an integral role in such grid environments and even more so in medical grids. Unfortunately, many grid middleware distributions, such as the well-known Globus Toolkit, lack the integration of the world-wide medical image communication standard Digital Imaging and Communication in Medicine (DICOM). Currently, the DICOM protocol first needs to be converted to the file transfer protocol (FTP) that is offered by the grid middleware. This effectively reduces most of the advantages and security an integrated network of DICOM devices offers. In this paper, a solution is proposed that adapts the DICOM protocol to the Globus grid security infrastructure and utilizes routers to transparently route traffic to and from DICOM systems. Thus, all legacy DICOM devices can be seamlessly integrated into the grid without modifications. A prototype of the grid routers with the most important DICOM functionality has been developed and successfully tested in the MediGRID test bed, the German grid project for life sciences.

Clinical Sleep Research is an inherent multidisciplinary field, as many health issues may affect a person's sleep conditions and sleep disorders may cause several health problems. Many patients with chronic sleep disorders suffer from different further medical conditions -- called multimorbidity. Due to the high variety of the reasons and the courses of sleep disorders, individual cases are difficult to compare. Therefore there is a high demand for sleep researchers to collaborate with each other to reach necessary participant numbers and multidisciplinary expertise. To date, inter-institutional sleep research is poorly supported by IT systems. In particular the heterogeneity and the quality variations within the acquired bio signal data -- caused by different bio signal recorders or different measurement procedures -- are impeding common bio signal data processing. In this manuscript we introduce a virtual research platform supporting inter-institutional data sharing and processing. The infrastructure is based on XNAT -- a free and open-source neuroimaging research platform -- a loosely coupled service oriented architecture and scalable virtualization in the backend. The system is capable of local pseudonymization of bio signal data, mapping to a standardized set of parameters and automatic quality assessment. Terms and quality measures are derived from the "Manual for the Scoring of Sleep and Associated Events" of the American Academy of Sleep Medicine, the de-facto standard for diagnostic bio signal analysis in sleep medicine.

Filamentary formations of acoustic cavitation bubbles in an ultrasonic resonator are recorded by high-speed stereoscopic means. The bubble locations and motions are reconstructed in three dimensions, and a velocity distribution of bubbles is obtained. Experimental bubble trajectories are compared to a one-to-one simulation by a particle modeling approach which shows reasonable agreement. Such investigations are important for a better understanding of the mechanisms taking place in applications of intense ultrasound in liquids, and for verification and improvement of particle modeling of cavitation bubbles.

Sonoluminescing single bubbles driven simultaneously by two harmonic frequencies were recently reported to increase the maximum light output up to a factor of 3 with respect to single mode excitation. In this paper, experimental and numerical results on single-bubble sonoluminescence (SBSL) in an air/water system using the fundamental mode of 25 kHz and the second harmonic at 50 kHz are presented. The region of light emission is mapped in the three-dimensional parameter space spanned by the two driving pressure amplitudes and their relative phase. Good agreement was seen between measured light output, maximum bubble radius, and stability boundaries and the numerical model which is based on spherical bubble oscillations regarding diffusive and shape stability. The maximum brightness was enhanced by a factor up to 2.5 with respect to single mode SBSL. However, long-term measurements reveal great variation of the emission at fundamental mode driven SBSL and of the boost factor reached with two frequencies. The overall brightness maxima of both excitation methods within a period of several hours turn out to show little difference.

Neurosciencific research is increasingly based on image analysis methods. Large sets of imaging data are processed using complex image analysis tools. While today magnetic resonance imaging (MRI) is widely used for both functional and anatomical analysis of the human brain, new imaging modalities are beginning to prove their capabilities for neurological research. Among them, optical coherence tomography (OCT) allows for noninvasive visualization of anatomical structures on a micrometer scale. Becoming a standard diagnostic tool in ophthalmology, it is of rising interest for neurological research. Crucial to all data analysis methods is the quality of the input data. The platform presented in this paper is designed for automatic quality assessment of retinal OCTs. It extends the image management platform XNAT by services to calculate and store quality measures. It is also extensible regarding new quality measure algorithms, allowing the developer to upload Matlab code, compile it for the infrastructure's hardware architecture and test it in the system. The image processing tools to calculate the quality measures are provided as a cloud-based service employing OpenStack as underlying IT infrastructure. The prototype implementation encompassing security and performance aspects are presented.

Neuroimaging is a field that benefits from distributed computing infrastructures (DCIs) to perform data processing and analysis, which is often achieved using Grid workflow systems. Collaborative research in neuroimaging requires ways to facilitate exchange between different groups, in particular to enable sharing, re-use and interoperability of applications implemented as workflows. The SHIWA project provides solutions to facilitate sharing and exchange of workflows between workflow systems and DCI resources. In this paper we present and analyse how the SHIWA Platform was used to implement various cases in which workflow exchange supports collaboration in neuroscience. The SHIWA Platform and the implemented solutions are described and analysed from a "user" perspective, in this case workflow developers and neuroscientists. We conclude that the platform in its current form is valuable for these cases, and we identify remaining challenges.

Personal Health Systems are developed for individualized diagnosis, prevention and treatment depending on the actual health-state of the regarding person with a minimum of interference of his or her daily life. They imply the continuous or at least frequent monitoring of the person's health status, subsequent evaluation and possible action initialization, may it be feedback to the patient, a message to the attending physician or the readjustment of a therapeutical device. In this paper, we propose personal health systems that combine new sensor and information technologies. They allow for extensive undisturbing biosignal acquisition at the person's home and comprehensive data processing and analysis using a grid infrastructure. The grid can be considered as a virtual space where people can share their personal health data with attending medical professionals and where an individual and adaptive set of processing and analysis tools is available in a uniform, easy-to-access and cost-efficient way. The current state of public IT networks and grid infrastructures may not allow real-time applications, while complex near-term postprocessing and decision support can be accomplished. The presented systems are dedicated to sleep related breathing disorders, but the underlying concept may also be suitable for other use-cases.

The Charite Grid Portal combines portal components from different groups and projects to provide domain researchers a gateway to Grid-based biomedical applications. Trusted users can securely access and employ Grid resources and services. In this paper, five portal components are presented: (1) The credential management administrates the user-credentials and authenticates them to the Grid. (2) The brain imaging data analysis (FSL) submits workflows to the Grid as part of Medical Image processing. (3) The integrated web services of Generic Workflow Execution Service (GWES) manage workflows executed by users in the Grid. (4) The data management component provides secure and efficient data management in a Grid environment, and enables high-speed data transports between user and Grid. (5) The lung sound analysis application provides twofold-pseudonymization before data-transferred to the Grid. The implementation as standardized portlets allows easy integration of specific components into different Grid portals such as the VO specific MediGRID and PneumoGrid portals.

IntroductionViscoelastic properties indicate structural alterations in biological tissues at multiple scales with high sensitivity. Magnetic Resonance Elastography (MRE) is a novel technique that directly visualizes and quantitatively measures biomechanical tissue properties in vivo. MRE recently revealed that early relapsing-remitting multiple sclerosis (MS) is associated with a global decrease of the cerebral mechanical integrity. This study addresses MRE and MR volumetry in chronic-progressive disease courses of MS.MethodsWe determined viscoelastic parameters of the brain parenchyma in 23 MS patients with primary or secondary chronic progressive disease course in comparison to 38 age- and gender-matched healthy individuals by multifrequency MRE, and correlated the results with clinical data, T2 lesion load and brain volume. Two viscoelastic parameters, the shear elasticity μ and the powerlaw exponent α, were deduced according to the springpot model and compared to literature values of relapsing-remitting MS.ResultsIn chronic-progressive MS patients, μ and α were reduced by 20.5% and 6.1%, respectively, compared to healthy controls. MR volumetry yielded a weaker correlation: Total brain volume loss in MS patients was in the range of 7.5% and 1.7% considering the brain parenchymal fraction. All findings were significant (PConclusionsChronic-progressive MS disease courses show a pronounced reduction of the cerebral shear elasticity compared to early relapsing-remitting disease. The powerlaw exponent α decreased only in the chronic-progressive stage of MS, suggesting an alteration in the geometry of the cerebral mechanical network due to chronic neuroinflammation.

Advanced visualization technologies are gaining major importance to interpret, present and manipulate high dimensional biomedical data. Since new health technologies are constantly increasing in complexity, adequate information processing is required for diagnostics, therapy planning and treatment. The combination of advanced visualization resources and biomedical grid infrastructures is a promising approach to build dynamic and scalable problem solving environments for such tasks. Visualization then becomes the main issue in system-user interaction, and is therefore a crucial factor in the usefulness of the complete system for the domain researchers. In this paper, two remote visualization approaches to utilize the capabilities of such an environment are presented and evaluated. Evaluation criteria are performance, usability, adaptivity and functionality. Both, an open source and a commercial solution are considered. While the proprietary software shows better performance, the open source solution offers the advantage for seamless grid integration.

The analysis of biosignals, such as the electroencephalogram EEG or the electrocardiogram (ECG), is essential for diagnosis in many medical areas, in particular sleep medicine and sleep research. A standard method in this field is the polysomnography, a multidimensional biosignal recording during the whole bedtime phase. Within the SIESTA project, a European multicenter study, comprehensive clinical and polysommnographic records from over 300 persons has been collected. To make the data available for researchers as reference for clinical research and development of new analysis tools, the SIESTA database is implemented into a grid infrastructure. To date, the complete data is stored into the grid and different algorithms for automated ECG analysis are implemented. The database can be queried and the matching data can be analysed on record level and collection level. The application is modelled as a workflow and integrated into the grid using a workflow manager. A graphical user interface is implemented as a grid portlet. It allows the initialization of new computation tasks as well as the monitoring and result-retrieval from already launched analyses.

Sleep related breathing disorders (SRBD) represent a major disease in sleep medicine. For diagnosis and therapy follow-up, extensive overnight investigations are required, encompassing long-term measurement of multiple biosignals in specialized sleep disorders centers. To date, evaluation of the examination is realized by comprehensive visual inspection of the data by an expert. To facilitate and improve diagnostics, current attemps in sleep research are made to develop computer aided diagnosis tools as well as to provide mobile sensor devices for biosignal measurement in the patient’s ambience.

Advanced visualization technologies are gaining major importance to allow presentation and manipulation of high dimensional data. Since new health technologies are constantly increasing in complexity, adequate information processing is required for diagnostics and treatment. Therefore, the German D-Grid initiative started to build visualization centers in 2008, which have recently been embedded into the existing compute and storage infrastructure. This paper describes an analysis of this infrastructure and the interplay with life science applications for 3D and 4D visualization and manipulation. Furthermore, the performance and business aspects regarding accounting, pricing and billing are investigated. The results show the viability and the opportunities for further optimization of this novel service approach and the possibilities for a sustainable business scenario.

Many scenarios in medical research are predestined for grid computing. Large amounts of data in complex medical image, biosignal and genome processing demand large computing power and data storage. Integration of distributed, heterogeneous data, e.g. correlation between phenotype and genotype data are playing an essential part in life sciences. Sharing of specialized software, data and processing results for collaborative work are further tasks which would strongly benefit from the use of grid infrastructures. However, two major barriers are identified in existing grid environments that prevent extensive use within the life sciences community: Extended security requirements and appropriate usability. To meet these requirements, the MediGRID project is enhancing the basic D-Grid infrastructure along with the implementation of prototype applications from different fields of biomedical research. In this paper, we focus on the developments for ease-of-use under consideration of different aspects of security. They encompass not only security within the grid infrastructure, but also the boundary conditions of network security oil the site of the research institutions. For medical grids, we propose a strictly web portal-based access to grid resources for end-users, with user-guiding, application specific. graphical interfaces. Different levels Of authorization are implemented, from fully authorized users to guests without certificate authentication in order to allow hands-on experience for potential grid users.

Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent feature model is built by supervised learning using a set of manually segmented needles. The feature space is spanned by common binary object features as size and eccentricity as well as imaging-system dependent features like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker lines are removed from the images. The segmentation itself is realized by scale-invariant classification using maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for biopsy needle localization in clinical prostate biopsy TRUS images.

Cloud computing has many benefits relevant to the healthcare industry. Although the adoption of cloud services for healthcare systems is increasing, employment of cloud services raises many security and privacy concerns for patients and healthcare providers. We still lack a clear set of requirements consented by the different stakeholders; here in particular IT and healthcare professionals. In this study, we examine whether user perspectives on requirements for e-health on the cloud are consistent with best practice guidelines and regulatory requirements. This work contributes to the requirements engineering phase for a secure e-health cloud framework developed in a European project (ASCLEPIOS, https://www.asclepios-project.eu/). We used qualitative analysis, based on in-depth interviews, to describe and characterize clinicians' perspectives on the requirements of cloud services for healthcare data security and privacy. We examined whether these user perspectives were in harmony with the regulatory framework of the General Data Protection Regulation (GDPR), and best practice guidelines of a relevant standard, ISO 18308:2011. Ten clinicians were identified and interviewed at six healthcare organizations in Norway, the Netherlands and Germany. While user perspectives were largely consistent with both GDPR and ISO, some concerning differences in access control were noted between large and small healthcare institutions.

As cloud adoption increases, so do the number of available cloud service providers. Moving complex applications between clouds can be beneficial - or other times necessary - but achieving this so-called cloud portability is rarely straightforward. This paper presents the adoption of OASIS TOSCA, a standard in the declarative description of cloud applications, to encourage and facilitate cloud portability in MiCADO, an application-level multi-cloud orchestration and auto-scaling framework. The interface to MiCADO is an Application Description Template, which draws from the TOSCA specification to describe an application in MiCADO. The generic design of these templates is presented and their applicability for achieving portability between different container and cloud environments is analysed and evaluated. A proof-of-concept where MiCADO serves as the deployment and execution engine for a Science Gateway in Sleep Healthcare is then described. In this proof-of concept, MiCADO facilitates the deployment of a complex healthcare application, which is then moved from one cloud service provider to another with only minimal changes to the template which originally described it. This TOSCA-based approach to templates in MiCADO encourages movement between clouds by making cloud portability more approachable.