Open Source in Imaging Informatics

The open source community within radiology is a vibrant collection of developers and users working on scores of collaborative projects with the goal of promoting the use of information technology within radiology for education, clinical, and research purposes. This community, which includes many commercial partners, has a rich history in supporting the success of the digital imaging and communication in medicine (DICOM) standard and today is pioneering interoperability limits by embracing the Integrating the Healthcare Enterprise. This article describes only a small portion of the more successful open source applications and is written to help end users see these projects as practical aids for the imaging informaticist and picture archiving and communication system (PACS) administrator

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field

Analysis of open-ended question responses reporting user satisfaction with library services using an interactive visualization tool

This paper discusses the major results and conclusions derived from the open-ended questions analysis, which was part of a 28 questions survey (25 close-ended questions, 3 open-ended questions) on academic scientist's information seeking behavior (ISB) and information use (IU) conducted at the University of North Carolina-Chapel Hill (UNC-CH) during the Spring of 2005. The university's academic scientists were asked to provide written responses expressing their perceptions about UNC-CH library and information services. The three open-ended questions were: (1) what are the positive aspects of service, (2) what are the shortcomings, and (3) what is one wish for future services (Hemminger, 2007). Nine-hundred sixty-nine (969) participants completed the survey. The participant comments were used to create a coding/classification schema of library services. Interactive Comment in Schema (ICIS), an interactive, web-based visualization tool, was created for displaying, analyzing, and sharing participant feedback among the university's librarians

Advanced automated PET image segmentation in radiation therapy

Manual segmentation of the metabolic tumour volume (MTV) in positron emission tomography (PET) imaging is subject to intra and inter-observer variability. Many PET based automatic segmentation algorithms (PETAS) have been proposed as solutions to this problem with machine-learned techniques showing promise for accurate MTV segmentation. However, no consensus has been reached on the optimal method for radiotherapy (RT) treatment planning, with the current American Association for Physcists in Medicine Task Group 211 and the International Atomic Energy Association advisory committees recommending that not one single PET-AS can be recommended for target volume delineation. This project, therefore, aimed to improve the MTV segmentation of a machine-learned PET-AS methodology called ATLAAS which has been proposed for standardised MTV segmentation by Berthon et al in Radiother Onc (2016). Berthon et al additionally validated the ATLAAS algorithm on diagnostic PET imaging in Radiother Onc (2017). However, it has not been validated externally or for the role of MTV segmentation during treatment. Intratreatment segmentation is challenging due to reduced metabolic uptake, tuiv mour to background ratio and reduced metabolic volume. Therefore, in this body of work, the performance of ATLAAS and 151 other PET-AS chosen from the literature, were evaluated for suitable MTV segmentation in PET imaging acquired after one cycle of neoadjuvant chemotherapy. This research resulted in the development of a new training dataset and demonstrated that ATLAAS can be used as a basis for adaptive radiotherapy and trained on imaging datasets outside of the original training cohort. However, this research still demonstrated that the performance of ATLAAS could be improved. Therefore, this led to an investigation into the inclusion of additional tumour characteristics in the development of the ATLAAS training model, in order to reduce the impact PET image resolution has on MTV segmentation. In this research, derived MTVs were compared to \ground truth" volumes derived from CT imaging. The results presented in this body of work, showed that interpolating PET imaging to the resolution of the CT image improved the performance of PET-AS segmentation and improved ATLAAS MTV segmentation by 19% and inclusion of one of the tumour features compactness one, compactness two or sphericity in the ATLAAS training model improved MTV segmentation by an additional 3%. As part of this body of work, the requirement for a standardised PET-AS method was demonstrated by developing prognostic models, using standardised imaging and tumour features, from the MTV derived by 9 PET-AS demonstrated by Berthon et al in Phys. Med. Biol (2017) to be promising for accurate MTV segmentation. This showed how segmentation of the MTV 120-80% Threshold in increments of 10%, Adaptive Thresholding, Region Growing, K-means Clustering with 2 and 3 clusters, Gaussian Fuzzy C-means with 3 and 4 clusters and Fuzzy-C means with 2 clusters v has a subsequent effect on patient risk stratification with patients changing risk stratification quartiles dependent upon the PET-AS used to derive the MTV

Scholarly Electronic Publishing Bibliography: 2007 Annunal Edition

This bibliography lists citations of English-language articles, books and other printed and electronic sources that are useful in understanding scholarly electronic publishing efforts on the Internet. Most sources have been published from 1990 through 2007; however, a limited number of key sources published prior to 1990 are also included

The Scholarly Electronic Publishing Bibliography: 2008 Annual Edition

The Scholarly Electronic Publishing Bibliography: 2008 Annual Edition presents over 3,350 English-language articles, books, and other printed and electronic sources that are useful in understanding scholarly electronic publishing efforts on the Internet. Most sources have been published from 1990 through 2008; however, a limited number of key sources published prior to 1990 are also included. Where possible, links are provided to works that are freely available on the Internet, including e-prints in disciplinary archives and institutional repositories. It is available under a Creative Commons Attribution-Noncommercial 3.0 United States License

Scholarly Electronic Publishing Bibliography: 2008 Annunal Edition

This bibliography lists citations of English-language articles, books and other printed and electronic sources that are useful in understanding scholarly electronic publishing efforts on the Internet. Most sources have been published from 1990 through 2008; however, a limited number of key sources published prior to 1990 are also included

Scholarly Electronic Publishing Bibliography 2010

The Scholarly Electronic Publishing Bibliography 2010 presents over 3,800 selected English-language articles, books, and other textual sources that are useful in understanding scholarly electronic publishing efforts on the Internet. It covers digital copyright, digital libraries, digital preservation, digital rights management, digital repositories, economic issues, electronic books and texts, electronic serials, license agreements, metadata, publisher issues, open access, and other related topics. Most sources have been published from 1990 through 2010. Many references have links to freely available copies of included works. It is under a Creative Commons Attribution-Noncommercial 3.0 United States License. Cite as: Bailey, Charles W., Jr. Scholarly Electronic Publishing Bibliography 2010. Houston: Digital Scholarship, 2011

Digital Scholarship 2009

The work is a bibliography of digital scholarship containing citations of articles, books, and technical reports on institutional repositories and scholarly electronic publishin