Medical imaging informatics has revolutionized healthcare practice. Images are now stored on a central server and can be accessed remotely through web technologies and standard formats. These images are accompanied by patient-identifiable metadata, allowing systems to execute queries based on that data and present it alongside the pixel data. Despite these advantages, this data has privacy constraints when sharing and reusing it in different scenarios. In research, there is an urgent need for large volumes of data to develop new technologies for diagnosis and therapy purposes. We propose a cloud-ready architecture to build a collaborative environment with anonymized images that can be safely used in research. The solution is based entirely on open-source components, allowing the construction of anonymization pipelines that process and forward images to distinct repositories. The proposal is resilient and scalable and has been tested in a production setting for several clinical studies and use cases.

Computer-assisted diagnosis has been advancing significantly with the proliferation of intelligent image analysis algorithms. These tools enhance medical image screening and diagnostic workflow by highlighting relevant features for doctors, resulting in faster and more accurate diagnoses. However, developing these tools requires large annotated image datasets for achieving acceptable performance. Moreover, developing the analysis algorithms requires knowledge about these technologies, often limited to medical staff. Finally, the integration of those solutions in clinical environments is still a hard barrier due to interoperability issues. This work proposes a solution that extends a Vendor-Neutral Archive for supporting the integration of AI tools for research and production. It makes use of two open-source components, Dicoogle and MONAI, resulting in a Web and DICOM compliant platform that uses active learning strategies for automatic image annotation and inference services.

Clinical pathology has been adopting digital technologies in the review process of the slides. This paradigm shift is being instigated by the advances in technology that allow pathologists to remotely access data banks and visualize high-resolution images with the support of cutting-edge imaging tools. Computer-assisted diagnosis is one of such improvements that is proliferating due to the recent advancements in deep learning technologies. In this clinical area, these tools are especially relevant, due to the big spatial resolution of these images. They can identify regions of interest or diagnostically relevant features on the image, improving the screening times. This article presents the use of active learning strategies to train an object detection model for the mitotic annotation use case, developed in the iPATH research project. The model was successfully integrated into a Pathology-PACS and is being actively used by pathologists for research purposes. Results regarding the evolution and development of this model are also presented in this work.

The use of digital imaging in medicine has become a cornerstone of modern diagnosis and treatment processes. The new technologies available in this ecosystem allowed healthcare institutions to improve their workflows, data access, sharing, and visualization using standardized formats. The migration of these services to the cloud enables a remote diagnostic environment, where professionals can review the studies remotely and engage in collaborative sessions. Despite the advantages of cloud-ready environments, their adoption has been slowed down by the demanding scenario high-resolution medical images pose. Some studies can have several gigabytes of data that need to be managed and consumed in the network. In this context, performance constraints of the software platforms can result in severe denial of clinical service. This work proposes a highly scalable cloud platform for extreme medical imaging scenarios. It provides scalability with auto-scaling mechanisms that allow dynamic adjustment of computational resources according to the service load.

In the last decade, digital technologies have been transforming clinical pathology, dematerializing the processes, improving the workflow, and promoting the development of decision support tools. The glass slides can be digitized with high-quality scanners and sent to network archives, opening doors to data sharing and universal access. Despite these advancements, however, the adoption of digital pathology solutions in production environments has been slowed down by the lack of standardization in the available software. Among the improved visualization features in digital pathology is the ability to capture a series of images at the same spatial location with varying focal planes, also known as Z-stack. This article proposes a full-stack, standard solution that efficiently supports the visualization of these 4D images. It is a Cloud-based architecture that supports Z-stack image management and visualization. The solution is made vendor-neutral by providing a pipeline for the integration of different image vendors.

Recent advancements in the acquisition of digital imaging modalities with high-throughput technologies, such as confocal laser scanner microscopy (CLSM) and focused-ion beam scanning electron microscopy (FIB-SEM), are providing researchers with unprecedented opportunities to collect massive amounts of multidimensional datasets. This data can be used to visualize the internal structure of tiny particles (mostly cells and organisms) or to develop analytic algorithms. Visualizing newly obtained multidimensional microscope imaging data is beyond the capabilities of traditional 3D visualization packages, as it carries much information in the form of additional dimensions. Typically, these extra dimensions correspond to space, time, and channels, which has driven the development of new visualization applications. In this article, we describe the design and implementation of an interactive web-based multidimensional 3D visualization tool for CLSM and FIB-SEM microscope imaging modalities. The proposed 3D visualization application accepts DICOM files as input and provides a variety of visualization choices ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. The solution performance was tested by uploading and rendering microscopy images of distinct modalities.

The use of medical imaging in clinical practice has increased dramatically in recent decades. The adoption and migration to the Cloud of medical imaging systems and services is an excellent opportunity for telemedicine, telework and collaborative work environments. However, the adoption of this paradigm has been slow in this scenario. While the migration has many advantages, it also introduces new challenges mainly related with data storage and management. One of most important open problems is with the efficient handling and transmission of large volumes of data. This issue is particularly critical if the service requests data regional redundancy and high scalability. In this context, this article proposes and describes a new architecture compliant with medical imaging requirements and following standard Cloud and medical imaging interfaces. The solution is based on Kubernetes, an open-source system to deploy, scale and manage containerized applications anywhere. The proposal includes an intelligent component for distributed management of the medical studies based on service policies. The result was a scalable Cloud-based medical imaging repository that can be deployed in a standard way in multiple providers that can achieve a better performance than single node.

In the last decade, deep learning has been transforming the healthcare scenario with the provision of new computer-assisted diagnosis tools in an unprecedented way. The spread of specialized hardware and software has been supporting this reality. The development of such predictive models, however, requires expertise and time. In this context, this article proposes and describes the implementation of a no-coding software framework for the automation of deep learning training processes, aiming to support the development of medical image diagnostic classifiers by healthcare professionals with limited expertise in deep learning. It is a web solution that allows the easy management of data, models, and training processes associated with user requests, where the benefits extend to non-experts in the machine learning field. It is an intuitive web solution that contains a set of additional modern tools like Automatic Machine Learning (AutoML) to aid design models and a ranking system to help improve and share them.

Because of its high-resolution visibility across bone tissue, digital whole slide imaging (WSI) in pathology has become increasingly popular in biomedicine for diagnostic, educational, and research purposes. The technology, however, is not yet sufficiently mature to support remote clinical practice in pathology. Several technical challenges have hampered the gradual rollout of telematics platforms. Most notably, there is a lack of system interoperability because scanner manufacturers have yet to adopt a standard for data format and communications processes, specifically the DICOM standard, which already supports WSI. At the moment, it is uncommon to find an institutional repository capable of acquiring, storing, and visualizing samples scanned by different vendors’ equipment. Although some vendors offer DICOM interfaces, the vast majority still use proprietary solutions. This article proposes a framework for multi-vendor data integration through the dicomization of proprietary images, including metadata, and optimization of the visualization process, with a focus on the stacked gigapixel WSI produced by new generation scanners. These images are difficult to preprocess and visualize in most environments due to their size, which is typically in the hundreds of Gigabytes range, and there are no reliable packages that can load, preprocess, and visualize these stacked WSI on a single platform. In this work, we created a simple automated pipeline that can read the majority of proprietary WSI images and focal planes, convert them to DICOM for preprocessing and visualization, and make them compatible with modern Web PACS platforms. The solution was tested with eight distinct proprietary files, two of which were stacked images, from acquisition to Web visualization, demonstrating good performance and efficient use of computational resources.

Nowadays, the medical imaging laboratories are an important piece in the diagnosis within the healthcare institutions. Every day, medical imaging modalities generate new data and the growth over the last recent years has been exponential. The storage and distribution of such data is orchestrated by the Picture Archiving and Communication System (PACS) but the current technologies are facing new challenges. On one hand, the massive amount of data requires a huge local infrastructure to handle the load, which may be expensive. On the other hand, the outsourcing of the data to cloud services may be slow and not compliant with regulations. It becomes urgent to investigate new ways to handle the storage and distribution of the medical images in an healthcare institution. In this work, we discuss the current solutions for the problem of storage and management of medical images. Furthermore, we introduce a solution based on a private network of nodes that can overcome problems as privacy, redundancy and high availability. The developed system offers features that totally match with the Digital Imaging and Communications in Medicine (DICOM) standard in terms of data integrity, immutability, and tolerance to failure. The results show that the proposed architecture performance are very similar with the baseline solutions. So, it reveals itself as a good solution considering the advantages pointed out.

In recent years, the quality of fluorescence microscopic imaging produced by a laser scanning microscope (LSM) system has been increased through the use of optical imaging techniques being used for small biological or nonbiological particles like instance, bacteria or cells in tissue samples. Currently, multiple manufactures provide LSM equipment able to capture single-layer or stacked images. However, the manufactures still using distinct data formats including proprietary ones, limiting the interoperability with third systems. In the clinical environment, the scanners need to be integrated with a vendor-neutral archive, storing images in a standard format and interface, making them accessible to healthcare professionals regardless of what proprietary system created it. Having distinct software solutions to manage the data is tiresome and time-consuming. This article proposes a normalization pipeline that can convert distinct vendor formats in a standard DICOM structure including pixel data and metadata. After conversion, the images are sent to a DICOM-compliant repository being able to be consumed in the network using normalized communication and visualization processes. The proposed solution is reliable and uses efficiently the less memory, a critical issue since the resolution of pathology whole-slide images can reach several gigapixels.

In the nineties, the adoption of the DICOM standard format in radiology departments brought numerous advantages to clinical practice. The setup of PACS with standard communication processes and data formats allowed the creation of central repositories, fast retrieval of images, visualization of images acquired with several modalities, and simultaneous access at distributed places. Nowadays, microscopy imagining faces the same normalization challenge with the proliferation of equipment that stores data in a proprietary format and provides dedicated visualization software. This reality severely limits the implementation of vendor-neutral archives with common visualization processes, conditioning the research work and its integration in clinical environments. This paper proposed a pipeline for the integration of multiple microscopy imaging modalities into the PACS-DICOM universe, including the numerous metadata elements. A proof-of-concept system was developed, for validation purposes, and integrated with the Dicoogle open-source PACS, providing image storage, metadata indexing and visualization.

Centralized identity and access management providers (IdAM) are a source of mistrust when deploying federated services. The access token is the main piece that carries a trusted signature between the IdAM and the third-party service. Our holder-of-key access token proposal aims to reduce the risk of token forgery from the IdAM by decentralizing the generation process via (t, n)-threshold cryptography. Nonetheless, implicit consent routes are still a requirement under current legislation; and, non-encrypted personal records are useful and many times required to the third-party service provider. Our token scheme and architecture can grant access to pseudonymised resources via explicit or implicit consents. The access token is publicly verifiable and is bound to a specific pseudonym and secret key. The token has no information that can disclose the true identity behind the pseudonym. The scheme is proven secure under reasonable assumptions and scalable from the experimental results.

Healthcare data for primary use (diagnosis) may be encrypted for confidentiality purposes; however, secondary uses such as feeding machine learning algorithms requires open access. Full anonymity has no traceable identifiers to report diagnosis results. Moreover, implicit and explicit consent routes are of practical importance under recent data protection regulations (GDPR), translating directly into break-the-glass requirements. Pseudonymisation is an acceptable compromise when dealing with such orthogonal requirements and is an advisable measure to protect data. Our work presents a pseudonymisation protocol that is compliant with implicit and explicit consent routes. The protocol is constructed on a (t,n)-threshold secret sharing scheme and public key cryptography. The pseudonym is safely derived from a fragment of public information without requiring any data-subject's secret. The method is proven secure under reasonable cryptographic assumptions and scalable from the experimental results.

Every year, the amount of data produced in health-care institutions in the field of medical imaging is increasing. The easy accessibility to digital modalities and the proliferation of diagnosis centers working in network of services, creates the need for very large repositories of data. Moreover, the institutions have redundant storage across the network and load balancing mechanism to improve access performance. NoSQL databases provide horizontal scalability which allows the system to grow easily and effortlessly. Document databases are particularly suitable to store the DICOM metadata and the JSON format is already supported by the standard. This paper, we discuss the use of document database model in the medical imaging field, trying to focus on both clinical and research environments. Moreover, we focused on the proposal of an architecture based on MongoDB to distribute the load between multiple nodes. The proposal was integrated with an open-source PACS and validated by simulating multiple operations over data distributed across multiple virtual locations, and the results are promising.

One of the most noticeable trends in healthcare over the last years is the continuous growth of data volume produced and its heterogeneity. In the medical imaging field, the evolution of digital systems is supported by the PACS concept and the DICOM standard. These technologies are deeply grounded in medical laboratories, supporting the production and providing healthcare practitioners with the ability to set up collaborative work environments with researchers and academia to study and improve healthcare practice. However, the complexity of those systems and protocols makes difficult and time-consuming to prototype new ideas or develop applied research, even for skilled users with training in those environments. Dicoogle emerges as a reference tool to achieve those objectives through a set of resources aggregated in the form of a learning pack. It is an open-source PACS archive that, on the one hand, provides a comprehensive view of the PACS and DICOM technologies and, on the other hand, provides the user with tools to easily expand its core functionalities. This paper describes the Dicoogle framework, with particular emphasis in its Learning Pack package, the resources available and the impact of the platform in research and academia. It starts by presenting an overview of its architectural concept, the most recent research backed up by Dicoogle, some remarks obtained from its use in teaching, and worldwide usage statistics of the software. Moreover, a comparison between the Dicoogle platform and the most popular open-source PACS in the market is presented.

Pseudonymisation is a major requirement in recent data protection regulations, and of especial importance when sharing healthcare data outside of the boundaries of the affinity domain. However, healthcare systems require important break-the-glass procedures, such as accessing records of patients in unconscious states. Our work presents a pseudonymisation protocol that is compliant with break-the-glass procedures, established on a (t, n)-threshold secret sharing scheme and public key cryptography. The pseudonym is safely derived from a fragment of public information without any private secret requirement. The protocol is proven secure and scalable under reasonable assumptions.

Digital pathology is a new branch of medical imaging referring to the aggregation of equipment and software to acquire, store and display microscopic images in a distributed network environment. This article proposes an architecture and describes the implementation of a collaborative pathology web platform. The solution brings the modern collaborative concept, common in social and business networks, into Digital Pathology workflows supported by a customised PACS-DICOM infrastructure. The system assures services like the creation of working sessions, users groups, access control to sessions, synchronisation of operations in a rich web interface, replaying of the actions performed in a session, among others. The solution data management is ensured by a PACS compliant with the DICOM standard, more concretely the recent Whole Slide Imaging format and the DICOM Web communication services.

The General Data Protection Regulation (GDPR) is lengthy and it is essential to resume the impacts of it in specific use-cases to diminish the gap between system developers and regulators. Computer-aided diagnosis is one of such use-cases with increased importance on clinical screening programs. The regulation has distinct mentions that affect automated-decision solutions and healthcare records. This work identifies the fundamental legal issues, challenges and opportunities for this scenario and propose architectural guidelines to tackle them. The result is purely theoretical, however it is based on known architectures such as signaling networks, already applied in the telecommunication sector.

The increasing challenges and requirements of medical image retrieval systems are leading the scientific community towards exploring modern representation methods as a means to improve clinical information retrieval as we know it. While current research tackles medical image retrieval through text-based, visual-based, or mixed approaches, representation learning can play an important role in improving retrieval capabilities by encoding medical image content into compact representations, addressing the problem of dimensionality. This paper introduces the potential of representation learning for the retrieval of high dimensionality imaging studies through automatically learned representations for regions of interest. Preliminary results are presented for feature learning through adversarial auto-encoding, based on the VISCERAL medical image retrieval benchmark.

Medical imaging laboratories are supported by information and communication systems commonly denominated as PACS, that encompasses technology for acquisition, archive, distribution and visualization of digital images in network. Concerning the data and workflow management, traditional solutions used in production provide a limited set of services usually configured at system installation. As result, healthcare institutions are not able to fully explore their infrastructure or adapt it to new operational requirements, either for clinical or research procedures. This article proposes a framework for services orchestration and workflow management in distributed medical imaging environments. It was designed for end-user usage and is accessible through a Web portal that allows to document, repeat and allocate procedures and tasks to correct resources, either from information systems or human interventions. It provides an abstraction layer for integration with distinct data sources through standard services, allows the creation of new services through orchestration of existent ones and the scheduling of tasks. Moreover, it includes a logging and alert mechanism integrated with email service. The solution was validated through the specification of two use cases that were deployed in production environment.

Digital medical imaging is a fundamental tool for improving medical practice workflows and supporting clinical diagnosis. Nowadays, healthcare institutions are usually very supported by information and communication systems that meet regular practice requirements. However, the usage of those platforms in collaborative, research and educational scenarios faces several problems. One of the key issues is related with patient data privacy, namely with concerns related with the visual anonymization of studies. In the neuroimaging field, this subject is more complex since, even after removing the patient's information from the images meta-data or burned in the pixel data, it is still possible to identify the patients through 3D reconstruction of the volume. This article proposes and describes the implementation of an end-user service that allows neuroimages facial de-identification of CT volumes, being fully interoperable with production repositories. The solution was validated using a public dataset and made available to the community through its integration with an open source archive server.

Cardiovascular diseases are the leading cause of death worldwide. These diseases are related with a broad range of factors but usually show high correlation with diminished left ventricle function, which can be evaluated by measuring the ventricular ejection fraction through transthoracic echocardiography (TTE), a cost-effective and highly portable first-line diagnosing technique. Ejection fraction (EF) is currently determined through a semi-automatic process that requires manual delineation of the left ventricle area both in a diastolic and systolic frame of the patient's exam. To remove this manual annotation step, which is both time-consuming and user dependent, automatic Computer-Aided Diagnosis (CAD) systems can be used. Herein, we propose the first steps for such a system that classifies ejection fraction in four classes, based on TTE exams, with the objective of automatically providing valuable information to physicians. Our classification method is based on a 3D-Convolutional Neural Network (3D-CNN) trained on a dataset constructed with exams from a cardiology reference center. The dataset creation consisted of three main steps: firstly, for each exam, cine-loops showing the apical 4 chambers view were manually selected; then, 30 sequential frames were extracted from each cine-loop; finally, each frame was pre-processed to mask burned-in metadata. The neural network was designed to explore concepts such as convolutions using asymmetric filters and residual learning blocks. The model was trained on a dataset with 4000 TTE exams and tested on a separate dataset containing 1600 TTE cases. We obtained an accuracy of 78% and a F1 score of 71.3% for unhealthy EF (below 45%), 63.3% for intermediate EF (45-55%), 72.3% for healthy EF (55-75%) and 54.6% for abnormally high EF (above 75%). These results are promising and show that convolutional neural networks can be applied to this domain. Furthermore, this work will serve as a foundation for future research where other relevant cardiac metrics will be determined.

The general trend of information access anywhere and anytime is also leading to the emergence of innovative medical imaging systems, adapted to this new reality. The HTML5 standard led Web applications to another software level, providing a set of features that allows developing professional Web-based medical imaging applications. However, despite the visualization quality that is already possible in HTML5 browsers, the performance is still an issue, due to the typical size of image studies. In this paper, we present a caching and prefetching solution that enriches the end-user experience in a Web-based DICOM viewer, by reducing data access latency of examinations under revision. We deployed the system in a radiology center for mammography screening, at a national level, and the results show that this technique significantly reduces the average examination access latency, during the reviewing process.

Medical imaging has been an essential contributor to high-quality medical decisions. In the past few years, the production of medical imaging data has grown impressively, thanks to the increasing number of imaging centers and higher resolution modalities. Keeping high availability and acceptable performance in this scenario raises new challenges related to storage, discovery and distribution of imaging data. Nowadays Picture Archiving and Communication System (PACS) must optimize these processes to the limit to cope with Big Data usage scenarios. In this regard, this work explores novel technologies to improve the performance of query and retrieve services in medical imaging context, ensuring always the compatibility with Digital Imaging and Communications in Medicine (DICOM) standard. The focus is the optimization of querying services. Namely, we conducted several controlled experiments to determine the best database model to support these services. More precisely, we studied the performance of a traditional PACS archive, based on a relational database, against a more recent NoSQL database. We used large datasets with 7 million medical images that represent accurately a year of medical practice. The result of this work is a set of guidelines for the correct usage of analyzed databases in big data medical imaging scenarios, including the advantages and limitations of each model.

Nowadays, medical imaging laboratories are supported by heterogeneous systems, including image repositories, acquisition devices, viewer workstations and other administrative information systems. They hold tremendous amounts of data resulting not only from imaging modalities, but also from patient diagnosis, treatment, and services management. Unfortunately, the interoperability between the different medical information systems is still a major limitation. Despite the existence of standards to support the distinct RIS and PACS applications, such as DICOM and HL7, the interoperability between them is, in most cases, limited to a few sets of information elements. As a result, the establishment of cooperative workflows or the integrated visualization of patient data is still compromised. Moreover, this scenario severely constraints the usage of these data for research and business analytics purposes, commonly referred as secondary uses of data. In this document, we propose a method for transforming echocardiography reports held by proprietary information systems into DICOM Structured Reports (SR), the gold standard for interoperability in medical imaging. As a result, reports, images, and associated metadata can be accessed and shared by all PACS applications in an integrated and structured manner. Furthermore, the large-scale federation of those elements has a tremendous interest for data analytics and secondary uses of data.

Web-based technologies have been increasingly used in picture archive and communication systems (PACS), in services related to storage, distribution, and visualization of medical images. Nowadays, many healthcare institutions are outsourcing their repositories to the cloud. However, managing communications between multiple geo-distributed locations is still challenging due to the complexity of dealing with huge volumes of data and bandwidth requirements. Moreover, standard methodologies still do not take full advantage of outsourced archives, namely because their integration with other in-house solutions is troublesome. In order to improve the performance of distributed medical imaging networks, a smart routing mechanism was developed. This includes an innovative cache system based on splitting and dynamic management of digital imaging and communications in medicine objects. The proposed solution was successfully deployed in a regional PACS archive. The results obtained proved that it is better than conventional approaches, as it reduces remote access latency and also the required cache storage space.

The interoperability of services and the sharing of health data have been a continuous goal for health professionals, patients, institutions, and policy makers. However, several issues have been hindering this goal, such as incompatible implementations of standards (e.g., HL7, DICOM), multiple ontologies, and security constraints. Cross-enterprise document sharing (XDS) workflows were proposed by Integrating the Healthcare Enterprise (IHE) to address current limitations in exchanging clinical data among organizations. To ensure data protection, XDS actors must be placed in trustworthy domains, which are normally inside such institutions. However, due to rapidly growing IT requirements, the outsourcing of resources in the Cloud is becoming very appealing. This paper presents a software proxy that enables the outsourcing of XDS architectural parts while preserving the interoperability, confidentiality, and searchability of clinical information. A key component in our architecture is a new searchable encryption (SE) scheme-Posterior Playfair Searchable Encryption (PPSE)-which, besides keeping the same confidentiality levels of the stored data, hides the search patterns to the adversary, bringing improvements when compared to the remaining practical state-of-the-art SE schemes.

Healthcare institutions are increasingly taking advantage of information and computational systems to enhance the efficiency and quality of their services. These IT systems are normally able to handle huge amounts of digital data, and to extract relevant fingerprints useful to improve the quality of clinical practice. However, building automatized processes to achieve this over multiple and heterogeneous databases is still a challenge. This paper presents a new approach able to collect and index information from distinct medical data sources, allowing us to identify important metrics to evaluate the performance and quality of clinical services. A case study combining information from ultrasound medical images and echocardiogram clinical reports is also presented.

The increasing adoption of medical imaging equipment in healthcare has been leading to a huge dispersion of data repositories and institutions. Although the quality of diagnostic and treatment is deeply dependent on the health information that is available for physicians, several legal and technological issues have hindered the integration of these data. One of such problems is because traditional medical imaging protocols do not perform well in inter-institutional scenarios. This paper describes a hybrid network platform for medical imaging systems that provides searching and retrieval over multiple centres. Three key components support the system: an indexing engine, a multicast framework and a cloud service. Using a peer-to-peer paradigm with security constraints, the platform gathers the information of medical imaging repositories hosted inside the institutions, allowing physicians to access data when and where they need it.

During the last decade, medical imaging services have assumed a central role in healthcare institutions, and they are nowadays a decisive factor for the quality of diagnostic and treatments. Health stakeholders and policy makers have been steadily adopting PACS and DICOM standard, simplifying interoperability between distinct equipment and institutions. To assist images' interpretation, several visualization solutions emerged. However, these applications are targeted to specific operating systems, hindering its ubiquitous use, in increasing web-based working environments. In this paper we present a Web-based DICOM viewer that was entirely developed with web technology, namely HTML5 and JavaScript. The result is a visualization station that is already in use in two medical imaging centres and that can be accessed through a common web browser, from any computer, mobile device, or operation system.

Document exchange communities set the ground for cross-organization cooperation. They enable the exchange of patient's documents across distinct health organizations. However, there are various challenges that must be overcame before deploying such communities, for instance the construction of the Enterprise Master Patient Index (EMPI) which maps the several patient identifiers of each domain. This paper describes the development of an interoperable distributed system that expedites the exchange of documents by taking care of the patient identities autonomously. The system automatically builds the EMPI leveraging the healthcare workflow (based on PIX and XDS-I) for validating the automatic linkages of the patient identifiers. The human validation is a consequence of user's interaction with cross-domain documents: distributing and attenuating the validation effort.

Medical imaging has been increasingly a computational matter, from image acquisition, to its storage, processing and visualization. The recent advances in cloud computing also represent a great opportunity to distribute and process imaging workflows. However, this combination brings a new set of challenges. In this paper we discuss why and how cloud technologies can become a future environment for the deployment of medical imaging services. Furthermore, we propose an architecture with technological approaches oriented to this demanding scenario, that deals well with critical issues such as security, communication latency and interoperability.

Over the last decades, information systems for medical imaging sharing are imposing themselves as important tools for the diagnostic and study of pathologies. One of the most important advantages of those systems is to allow widespread sharing and remote access to medical data. Nevertheless, there is no simple solution for imaging data exchange between multiple places due to bureaucratic and technical issues. The paradigm introduced by Dicoogle project potentiates queries over a set of distributed repositories, which are logically indexed as a single federate unit. This paper describes a Cloud-based relay service that acts as a bridge of communications between the different institutions, allowing the community to access, share and discover imaging records.

Mobile technologies are increasingly important components in telemedicine systems and are becoming powerful decision support tools. Universal access to data may already be achieved by resorting to the latest generation of tablet devices and smartphones. However, the protocols employed for communicating with image repositories are not suited to exchange data with mobile devices. In this paper, we present an extensible approach to solving the problem of querying and delivering data in a format that is suitable for the bandwidth and graphic capacities of mobile devices. We describe a three-tiered component-based gateway that acts as an intermediary between medical applications and a number of Picture Archiving and Communication Systems (PACS). The interface with the gateway is accomplished using Hypertext Transfer Protocol (HTTP) requests following a Representational State Transfer (REST) methodology, which relieves developers from dealing with complex medical imaging protocols and allows the processing of data on the server side.