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.

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.