Findaureus: An open-source application for locating Staphylococcus aureus in fluorescence-labelled infected bone tissue slices.

Staphylococcus aureus (S. aureus) is a facultative pathogenic bacterium that can cause infections in various tissue types in humans. Fluorescence imaging techniques have been employed to visualize the bacteria in ex-vivo samples mostly in research, aiding in the understanding of the etiology of the pathogen. However, the multispectral images generated from fluorescence microscopes are complex, making it difficult to locate bacteria across image files, especially in consecutive planes with different imaging depths. To address this issue, we present Findaureus, an open-source application specifically designed to locate and extract critical information about bacteria, especially S. aureus. Due to the limited availability of datasets, we tested the application on a dataset comprising fluorescence-labelled infected mouse bone tissue images, achieving an accuracy of 95%. We compared Findaureus with other state-of-the-art image analysis tools and found that it performed better, given its specificity toward bacteria localization. The proposed approach has the potential to aid in medical research of bone infections and can be extended to other tissue and bacteria types in the future.

Intra-Query Parallelism for a Scalable and Responsive Web-Based Digital Pathology Viewer.

Computational systems are successfully used in distinct areas of health. However, the use of digital imaging in pathology is taking its first steps when compared with the radiology sector. For instance, inter-institutional web platforms interoperable with various scanners through standard communications are very rare. This is due to the fact that the technology is only now being mature enough to meet the major challenges of this sector in terms of data storage and data access for efficient visualization of images with several gigabytes. The remote access to those images proves to be a challenge in an open and heterogeneous environment. This paper proposes a scalable and efficient architecture for storing and dynamic data retrieval on distributed large-scale systems. The adopted methodology relies on intra-query parallelism to retrieve a large number of image segments in a scalable distributed environment.

A Cloud-Ready Architecture for Shared Medical Imaging Repository.

Nowadays usage paradigms of medical imaging resources are requesting vendor-neutral archives, accessible through standard interfaces, with multi-repository support. Regional repositories shared by distinct institutions, tele-radiology as a service at cloud, teaching, and research archives are illustrative examples of this new reality. However, traditional production environments have a server archive instance per functional domain where every registered client application has access to all studies. This paper proposes an innovator ownership concept and access control mechanisms that provide a multi-repository environment and integrates well with standard protocols. A secure accounting mechanism for medical imaging repositories was designed and instantiated as an extension of a well-known open-source archive. A new web service layer was implemented to provide a vendor-neutral solution compliant with modern DICOM Web protocols for storage, search, and retrieval of medical imaging data. The concept validation was done through the integration of proposed architecture in an open-source solution. A quantitative assessment was performed for evaluating the impact of the mechanism in the usual DICOM Web operations. This article proposes a secure accounting architecture able to easily convert a standard medical imaging archive server in a multi-repository solution. The proposal validation was done through a set of tests that demonstrated its robustness and usage feasibility in a production environment. The proposed system offers new services, fundamental in a new era of cloud-based operations, with acceptable temporal costs.

A Recommender System Based on Cohorts' Similarity.

Aiming to better understand the genetic and environmental associations of Alzheimer's disease, many clinical trials and scientific studies have been conducted. However, these studies are often based on a small number of participants. To address this limitation, there is an increasing demand of multi-cohorts studies, which can provide higher statistical power and clinical evidence. However, this data integration implies dealing with the diversity of cohorts structures and the wide variability of concepts. Moreover, discovering similar cohorts to extend a running study is typically a demanding task. In this paper, we present a recommendation system to allow finding similar cohorts based on profile interests. The method uses collaborative filtering mixed with context-based retrieval techniques to find relevant cohorts on scientific literature about Alzheimer's diseases. The method was validated in a set of 62 cohorts.

Biomedical Informatics - How to Choose the Best Tool for Each Task.

The ever-increasing number of bioinformatics software tools that are publicly available, is leading to greater expectations about its regular use in clinical practice. However, from the end-users' perspective, they face many time the challenge of choosing the right tool for each task, from a panoply of solutions that have been developed over the years. In this paper, we propose a benchmarking methodology, based on a set of performance indicators, which can be used to identify the best methods and tools for each particular use case, both in research as in clinical practice.

General guidelines for biomedical software development.

Most bioinformatics tools available today were not written by professional software developers, but by people that wanted to solve their own problems, using computational solutions and spending the minimum time and effort possible, since these were just the means to an end. Consequently, a vast number of software applications are currently available, hindering the task of identifying the utility and quality of each. At the same time, this situation has hindered regular adoption of these tools in clinical practice. Typically, they are not sufficiently developed to be used by most clinical researchers and practitioners. To address these issues, it is necessary to re-think how biomedical applications are built and adopt new strategies that ensure quality, efficiency, robustness, correctness and reusability of software components. We also need to engage end-users during the development process to ensure that applications fit their needs. In this review, we present a set of guidelines to support biomedical software development, with an explanation of how they can be implemented and what kind of open-source tools can be used for each specific topic.

An efficient architecture to support digital pathology in standard medical imaging repositories.

In the past decade, digital pathology and whole-slide imaging (WSI) have been gaining momentum with the proliferation of digital scanners from different manufacturers. The literature reports significant advantages associated with the adoption of digital images in pathology, namely, improvements in diagnostic accuracy and better support for telepathology. Moreover, it also offers new clinical and research applications. However, numerous barriers have been slowing the adoption of WSI, among which the most important are performance issues associated with storage and distribution of huge volumes of data, and lack of interoperability with other hospital information systems, most notably Picture Archive and Communications Systems (PACS) based on the DICOM standard. This article proposes an architecture of a Web Pathology PACS fully compliant with DICOM standard communications and data formats. The solution includes a PACS Archive responsible for storing whole-slide imaging data in DICOM WSI format and offers a communication interface based on the most recent DICOM Web services. The second component is a zero-footprint viewer that runs in any web-browser. It consumes data using the PACS archive standard web services. Moreover, it features a tiling engine especially suited to deal with the WSI image pyramids. These components were designed with special focus on efficiency and usability. The performance of our system was assessed through a comparative analysis of the state-of-the-art solutions. The results demonstrate that it is possible to have a very competitive solution based on standard workflows.

Challenges and Opportunities for Exploring Patient-Level Data.

The proper exploration of patient-level data will pave the way towards personalised medicine. To better assess the state of the art in this field we identify the challenges and uncover the opportunities for the exploration of patient-level data through the review of well-known initiatives and projects focusing on the exploration of patient-level data. These cover a broad array of topics, from genomics to patient registries up to rare diseases research, among others. For each, we identified basic goals, involved partners, defined strategies and key technological and scientific outcomes, establishing the foundation for our analysis framework with four pillars: control, sustainability, technology, and science. Substantial research outcomes have been produced towards the exploration of patient-level data. The potential behind these data will be essential to realise the personalised medicine premise in upcoming years. Hence, relevant stakeholders continually push forward new developments in this domain, bringing novel opportunities that are ripe for exploration. Despite last decade's translational research advances, personalised medicine is still far from being a reality. Patients' data underlying potential goes beyond daily clinical practice. There are miscellaneous challenges and opportunities open for the exploration of these data by academia and business stakeholders.