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In the past decade, the medical field is witnessing a greater need and the subsequent involvement of technology and automation. Skin cancer detection is one particular area in the medical field that is in dire need of such automation because day by day, the complexity of skin cancer is increasing and it is becoming more difficult to depend on specialized doctors at all instances. The aim of this work is to create a skin cancer detection system using DICOM images and multi-column convolutional neural networks (CNNs). DICOM dataset was chosen because it contains additional information about a patient which is not available in raw images, like age, gender, special medical conditions, time period, etc. This work uses the ISIC 2020 dataset which contains DICOM formatted data of cancerous and non-cancerous images of the outer skin. The images are trained in a separate multi-column CNN architecture, and simultaneously the additional information, or tag information, is trained in a separate dense network, then the parameters from these two sub-models are concatenated to give a single prediction. For comparison purpose, three other models were developed, one based on raw images alone, one based on preprocessed images and one based on preprocessed images alone with a multi-column CNN network. The following evaluation metrics are recorded and compared for all models: accuracy (training, validation and testing), F1-score, specificity and root-mean-square error (RMSE). Additionally, a comparative analysis is carried out with a previous work on the same field published in 2019 that utilized a MobileNet architecture. The proposed model achieved high accuracy when it is compared with other models. Moreover, the model also provide the results for a tenfold cross-validation carried out and to highlight the specific data split that yields the best results for training, validation and testing accuracies. [ABSTRACT FROM AUTHOR]

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Objective: Radiogenomics investigates the use of radiomics and genomics features in clinical decision-making. The purpose of this study is to classify a clinical outcome by using radiomics and genomics features. The performances of different classification methods are compared and the effect of feature selection on classification performance is investigated. Material and Methods: Non-small cell lung cancer dataset from The Cancer Imaging Archive was used. The type of histology was selected as binary clinical outcome for classification. This dataset contains computed tomography images and RNA-sequence gene expressions. To standardize features, z scaling was applied to radiomics features and logarithmic transformation was applied to genomics features. Data was divided into 70% train set and 30% test set. Classification was carried out by modeling only radiomics features, only genomics features, and radiomics and genomics features together. Elastic net, random forest, support vector machines, and XGBoost algorithms were used for classification. Different feature selection approaches were explored to see the effect of feature selection on classification. Performance measures were calculated by using the test set. Results: The use of radiomics and genomics features improved the classification performance of random forest and XGBoost when feature selection was either not applied or when AUC was used for the feature selection method and elastic net when Recursive Feature Elimination was used for feature selection. Conclusion: Feature selection-based classification approach has a limited impact on model performance. Also, integration of two different data sources does not result in higher performance for every classification method. [ABSTRACT FROM AUTHOR]

ObjectivesTo determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine.MethodsIn a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation.ResultsOf the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78.ConclusionsThe radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

IntroductionTwo-dimensional strain analysis is a powerful analysis modality, however, clinical utilization has been limited by variability between different analysis systems and operators. We compared strain in adults and children using vendor-specific and vendor-independent software to evaluate variability.MethodsOne hundred and ten subjects (50/110 pediatric, 80/110 normal left ventricular function) had echocardiograms with a General Electric ultrasound scanner between September 2010 and January 2012. Left ventricular longitudinal strain was derived with EchoPAC (General Electric, v10.8.1), a vendor-specific software, and Velocity Vector Imaging (Siemens, v3.5), which is vendor-independent. Three independent readers analyzed all the echocardiograms yielding 330 datasets.ResultsMean left ventricular global longitudinal Lagrangian strain was -18.1 ± SD 4.4% for EchoPAC and -15.3 ± SD 4.1% for Velocity Vector Imaging. Velocity Vector Imaging yielded lower absolute global longitudinal Lagrangian strain by mean 2.9 (±SD 2.7, p 3-5 strain points on repeat measurements. Strain values between the systems are not interchangeable.

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies. Our hypothesis is that if users and developers had access to easy to use tools for migrating proprietary formats to the open DICOM standard, then more tools would be developed as DICOM first implementations. Methods: The technology we present here is dicom_wsi, a Python based toolkit for converting any slide capable of being read by the OpenSlide library into DICOM conformant and validated implementations. Moreover, additional postprocessing such as background removal, digital transformations (e.g., ink removal), and annotation storage are also described. dicom_wsi is a free and open source implementation that anyone can use or modify to meet their specific purposes. Results: We compare the output of dicom_wsi to two other existing implementations of WSI to DICOM converters and also validate the images using DICOM capable image viewers. Conclusion: dicom_wsi represents the first step in a long process of DICOM adoption for WSI. It is the first open source implementation released in the developer friendly Python programming language and can be freely downloaded at https://github.com/Steven N Hart/dicom_wsi. [ABSTRACT FROM AUTHOR]

Background: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge., Objective: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research., Methods: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data., Results: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings., Conclusions: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions., (©Hyerim Ji, Seok Kim, Leonard Sunwoo, Sowon Jang, Ho-Young Lee, Sooyoung Yoo. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 12.07.2024.)

Importance: Medical imaging increases the workload involved in writing reports. Given the lack of a standardized format for reports, reports are not easily used as communication tools. Objective: During medical team-patient communication, the descriptions in reports also need to be understood. Automatically generated imaging reports with rich and understandable information can improve medical quality. Design, setting, and participants: The image analysis theory of Panofsky and Shatford from the perspective of image metadata was used in this study to establish a medical image interpretation template (MIIT) for automated image report generation. Main outcomes and measures: The image information included digital imaging and communications in medicine (DICOM), reporting and data systems (RADSs), and image features used in computer-aided diagnosis (CAD). The utility of the images was evaluated by a questionnaire survey to determine whether the image content could be better understood. Results: In 100 responses, exploratory factor analysis revealed that the factor loadings of the facets were greater than 0.5, indicating construct validity, and the overall Cronbach's alpha was 0.916, indicating reliability. No significant differences were noted according to sex, age or education. Conclusions and relevance: Overall, the results show that MIIT is helpful for understanding the content of medical images., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to develop and verify a personal computer-based software tool for calculating uniformity indices of gamma camera. Materials and Methods: The program was developed in MATLAB R2013b under Microsoft Windows operating system. Noise-less digital phantoms with known uniformity parameters were used to verify the accuracy of the program. Two hundred and forty-four Co-57 flood source images were acquired on Symbia T6 and Discovery nuclear medicine/computed tomography 670. The uniformity indices of these images were determined with their respective vendor's software and also by the tool developed. Bland-Altman plots were used for measuring the agreements between the developed program and the vendor's program for the calculation of uniformity indices. Results: The tool for calculating uniformity indices was found to be accurate. Uniformity indices measured with the tool revealed a very good correlation with vendor's software based on Bland-Altman analysis, as almost all measurements were within the ±2 standard deviation range. Conclusion: The software tool for calculation of uniformity indices is accurate, and the uniformity indices calculated by it are in agreement with uniformity indices calculated by the vendor's software. [ABSTRACT FROM AUTHOR]

Despite recently organized Digital Imaging and Communications in Medicine (DICOM) testing and demonstration events involving numerous participating vendors, it is still the case that scanner manufacturers, software developers, and users continue to depend on proprietary file formats rather than adopting the standard DICOM whole slide microscopic image object. Many proprietary formats are Tagged Image File Format (TIFF) based, and existing applications and libraries can read tiled TIFF files. The sluggish adoption of DICOM for whole slide image encoding can be temporarily mitigated by the use of dual-personality DICOM-TIFF files. These are compatible with the installed base of TIFF-based software, as well as newer DICOM-based software. The DICOM file format was deliberately designed to support this dual-personality capability for such transitional situations, although it is rarely used. Furthermore, existing TIFF files can be converted into dual-personality DICOM-TIFF without changing the pixel data. This paper demonstrates the feasibility of extending the dual-personality concept to multiframe-tiled pyramidal whole slide images and explores the issues encountered. Open source code and sample converted images are provided for testing.

As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products.

Business Intelligence approaches such as process mining can be applied to the healthcare domain in order to gain insight into the complex processes taking place. Disclosing asis processes helps identify room for improvement and answers questions from medical professionals. Existing approaches are based on proprietary log data as input for mining algorithms. Integrating the Healthcare Enterprise (IHE) defines in its Audit Trail and Node Authentication (ATNA) profile how real-world events must be recorded. Since IHE is used by many healthcare providers throughout the world, an extensive amount of log data is produced. In our research we investigate if audit trails, generated from an IHE test system, carry enough content to successfully apply process mining techniques. Furthermore we assess the quality of the recorded events in accordance with the maturity level scoring system. A simplified simulation of the organizational workflow in a radiological practice is presented. Based on this simulation a process miing task is conducted. [ABSTRACT FROM AUTHOR]

Introduction: The use of open source technologies to create collaboration platforms can produce huge advantages with small investment. Materials and Methods: We set up a telemedicine network for a healthcare district with typical centralization issues of developing countries. Our network was built using broadband Internet connection, and the digital divide in rural areas was reduced by means of wireless Internet connection. A software infrastructure was deployed on the network to implement the collaboration platform among different healthcare facilities. Results: We obtained an integrated platform with modest investment in hardware and operating systems and no costs for application software. Messaging, content management, information sharing, and videoconferencing are among the available services of the infrastructure. Furthermore, open source software is managed and continuously updated by active communities, making it possible to obtain systems similar to commercial ones in terms of quality and reliability. Conclusions: As the use of free software in public administration is being widely promoted across the European Union, our experience may provide an example to implement similar infrastructures in the field of healthcare and welfare. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, an attempt is made to provide two level securities for medical images using watermarking and encryption. Watermarking provides security and encryption, authenticates medical image and provides integrity. Watermarking is performed by using a new nontensor product wavelet filter banks, which have the ability to reveal singularities in different directions. Natural image is taken as the original image and the medical image is taken as a watermark image. The medical image is embedded into the LH sub band of the natural image. The encryption is performed by RSA, AES, and RC4 algorithms. A qualitative comparative analysis of the proposed algorithm is done for different encryption methods. The proposed algorithm has the ability to withstand different attacks like noise, rotation, contrast, and brightness attacks. Performance analysis is made by calculating PSNR, SSIM, NC, and CV. [ABSTRACT FROM AUTHOR]

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world wide, including many research sites in the United States that are funded by the National Institutes of Health. [ABSTRACT FROM AUTHOR]

The Digital Imaging and Communications in Medicine (DICOM) is the standard for encoding and communicating medical imaging information. It is used in radiology as well as in many other imaging domains such as ophthalmology, dentistry, and pathology. DICOM information objects are used to encode medical images or information about the images. Their usage outside of the imaging department is increasing, especially with the sharing of medical images within Electronic Health Record systems. However, learning DICOM is long and difficult because it defines and uses many specific abstract concepts that relate to each other. In this paper, we present an approach, based on problem solving, for teaching DICOM as part of a graduate course on healthcare information. The proposed approach allows students with diversified background and no software development experience to grasp a large breadth of knowledge in a very short time. [ABSTRACT FROM AUTHOR]

Copyright of Strahlentherapie und Onkologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is aimed to present a novel compartmental PACS model for the automatic object-oriented integration and visualization of heterogeneous data for the multidisciplinary biomedical studies in the midst of the imaging services in radiology department. The generic PACS is conceptually partitioned into two compartments: service and integration. The service compartment supports the routine imaging service by connecting the imaging modalities to the web client workstations through a single fault-tolerant hardware. The integration compartment is synthesized by open source and open standard software tools to perform the long-term archiving of imaging cases and to integrate the images with the related data from other clinical disciplines. The prototype of the compartmental PACS model has been successfully implemented in the Department of Health Technology and Informatics (HTI) at the Hong Kong Polytechnic University (PolyU). Multi-disciplinary study on a cardiovascular case is considered as an example in this paper to demonstrate the seamless integration and web-based visualization of heterogeneous data from radiology, ophthalmology, cardiology and hematology. The implementation of the compartmental PACS model demonstrates a deliberative system design for allocating mission-critical components at the clinical frontline of imaging services and long-term archiving components at the backend. The integrative feature of object-oriented long-term archive addresses the comprehensive needs of patient-centered multidisciplinary biomedical studies. [ABSTRACT FROM AUTHOR]

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols. [ABSTRACT FROM AUTHOR]

Abstract: Information and communication technology in healthcare promises optimized patient care while ensuring efficiency and cost-effectiveness. However, the promised results are not yet achieved; the healthcare process requires analysis and radical redesign to achieve improvements in care quality and productivity. Healthcare process reengineering is thus necessary and involves modeling its workflow. Even though the healthcare process is very large and not very well modeled yet, its sub-processes can be modeled individually, providing fundamental pieces of the whole model. In this paper, we are interested in modeling the radiology interpretation process that results in generating a diagnostic radiology report. This radiology report is an important clinical element of the patient healthcare record and assists in healthcare decisions. We present the radiology interpretation process by identifying its boundaries and by positioning it on the large healthcare process map. Moreover, we discuss an information data model and identify roles, tasks and several information flows. Furthermore, we describe standard frameworks to enable radiology interpretation workflow implementations between heterogeneous systems. [Copyright &y& Elsevier]

A few years ago, the Digital Imaging and Communications in Medicine standard introduced a network transaction that is initiated by modality equipment, mainly at the beginning and at the end of the acquisition. This transaction, the Modality Performed Procedure Step (MPPS), is sent to the Picture Archiving and Communication System and/or to the Radiology Information System. It carries information about what really has been performed by the modality equipment during acquisition. In this paper, we present MPPS and discuss its benefits. We show how MPPS enables efficient radiology workflow and how it ensures accuracy and completeness of imaging information. We think our paper helps bridge the gap between MPPS implementation and deployment. By understanding all the MPPS benefits, the end user becomes aware of the great enhancement in patient care that this transaction provides. [ABSTRACT FROM AUTHOR]

The picture archiving and communication system (PACS) technology reaches its 10th anniversary. Retrospectively no one could foresee the impact the PACS would have to the health care enterprise, but it is common consent today, that PACS is the key technology crucial to daily clinical image operations and especially to image related basic and clinical research. During the past 10 years the PACS has been matured from a research and developmental stage into commercial products which are provided by all major modality and health care equipment vendors. The PACS, originally implemented in the Radiology Department, needs to grow and has already carried well beyond departmental limits conquering all image relevant areas inside the hospital. During the past 10 years a dramatic development in imaging techniques especially within MRI emerged. Advanced 3D- and 4D-MR imaging techniques result in much more images and more complex data objects than ever before which need to be implemented into the existing PACS. These new imaging techniques require intensive post-processing apart from the imaging modality which need to be integrated into the image workflow and the PACS implementation. Along with these new imaging techniques new clinical applications, e.g. stroke detection, and research applications, e.g. study of heart and brain function, in Neurology and Cardiology require changes to the traditional PACS concept. Therefore inter-disciplinary image distribution will become the high-water mark for the next 10 years in the PACS endeavor. This paper focuses on one new advanced imaging technique, functional magnetic resonance imaging (fMRI), and discusses how fMRI data is defined, what fMRI requires in terms of clinical and research applications and how to implement fMRI in the existing PACS. [Copyright &y& Elsevier]

Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world-wide, including many research sites in the United States that are funded by the National Institutes of Health.

A clinically meaningful use of structured reporting, which in the opinion of numerous scientific societies and experts is a very important prerequisite for the further development of radiological findings, especially under quality aspects, requires corresponding standards for implementation in IT systems. In addition to DICOM ("digital imaging and communication in medicine"), these are other standards for coding, for example RadLex ("radiological lexicon") or the specification of so-called interoperability profiles, as they are being developed by IHE ("integrating the healthcare enterprise"). The management of radiology report templates (MRRT) profiles is the central building block for this. The building blocks for efficient IT implementation, which also allow harmonization, for example at a national level, are currently available. Users in radiology should familiarize themselves with them and demand appropriate solutions from manufacturers., (© 2021. Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies. Our hypothesis is that if users and developers had access to easy to use tools for migrating proprietary formats to the open DICOM standard, then more tools would be developed as DICOM first implementations. Methods: The technology we present here is dicom_wsi, a Python based toolkit for converting any slide capable of being read by the OpenSlide library into DICOM conformant and validated implementations. Moreover, additional postprocessing such as background removal, digital transformations (e.g., ink removal), and annotation storage are also described. dicom_wsi is a free and open source implementation that anyone can use or modify to meet their specific purposes. Results: We compare the output of dicom_wsi to two other existing implementations of WSI to DICOM converters and also validate the images using DICOM capable image viewers. Conclusion: dicom_wsi represents the first step in a long process of DICOM adoption for WSI. It is the first open source implementation released in the developer friendly Python programming language and can be freely downloaded at https:// github.com/Steven N Hart/dicom_wsi. [ABSTRACT FROM AUTHOR]

We present a roadmap for integrating artificial intelligence (AI)-based image analysis algorithms into existing radiology workflows such that (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI, and (2) radiologists' feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where (1) research enables the visualization of AI-based results/annotations by radiologists without generating new patient records; (2) production allows the AI-based system to generate results stored in an institution's picture-archiving and communication system; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.e., detection of brain metastases with T1-weighted contrast-enhanced three-dimensional MRI) illustrates the deployment details of a particular AI-based application according to the aforementioned maturity levels. It is shown that the given AI application significantly improves with feedback coming from radiologists; the number of incorrectly detected brain metastases (false positives) decreases from 14.2 to 9.12 per patient with the number of subsequently annotated datasets increasing from 93 to 217 as a result of radiologist adjudication., (© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.)

Despite recently organized Digital Imaging and Communications in Medicine (DICOM) testing and demonstration events involving numerous participating vendors, it is still the case that scanner manufacturers, software developers, and users continue to depend on proprietary file formats rather than adopting the standard DICOM whole slide microscopic image object. Many proprietary formats are Tagged Image File Format (TIFF) based, and existing applications and libraries can read tiled TIFF files. The sluggish adoption of DICOM for whole slide image encoding can be temporarily mitigated by the use of dual-personality DICOM-TIFF files. These are compatible with the installed base of TIFF-based software, as well as newer DICOM-based software. The DICOM file format was deliberately designed to support this dual-personality capability for such transitional situations, although it is rarely used. Furthermore, existing TIFF files can be converted into dual-personality DICOM-TIFF without changing the pixel data. This paper demonstrates the feasibility of extending the dual-personality concept to multiframe-tiled pyramidal whole slide images and explores the issues encountered. Open source code and sample converted images are provided for testing., Competing Interests: There are no conflicts of interest.

As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products. [ABSTRACT FROM AUTHOR]