The Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) is a standard for structural interchange and archiving of clinical trial data. However, ODM insufficiently supports visualization (e.g., dropdown vs. checkbox), layout (e.g., horizontal vs. vertical), and functionality (e.g., hiding of fields). In this work, the Operational Data Representation (ODR) is defined as a semi-formal specification language. ODR extends ODM by a presentation layer, whereby ODM is still used for data binding. As proof of concept, a software framework of ODR rendering engines is implemented on various devices (web, mobile applications, paper-based). A converter for OpenClinica eCRFs is developed to simplify ODR specification. Using ODR, a public repository of more than 4,000 ODM-based eCRFs is rendered correctly on all runtime systems. Therefore, ODR allows eCRF interchange across heterogeneous devices and systems on the visual and the functional level. It will simplify electronic patient-reported outcome (ePRO).

Providing eligibility, efficacy and security evaluation by quantitative and qualitative disease findings, medical imaging has become increasingly important in clinical trials. Here, subject’s data is today captured in electronic case reports forms (eCRFs), which are offered by electronic data capture (EDC) systems. However, integration of subject’s medical image data into eCRFs is insufficiently supported. Neither integration of subject’s digital imaging and communications in medicine (DICOM) data, nor communication with picture archiving and communication systems (PACS), is possible. This aggravates the workflow of the study personnel, in special regarding studies with distributed data capture in multiple sites. Hence, in this work, a system architecture is presented, which connects an EDC system, a PACS and a DICOM viewer via the web access to DICOM objects (WADO) protocol. The architecture is implemented using the open source tools OpenClinica, DCM4CHEE and Weasis. The eCRF forms the primary endpoint for the study personnel, where subject’s image data is stored and retrieved. Background communication with the PACS is completely hidden for the users. Data privacy and consistency is ensured by automatic de-identification and re-labelling of DICOM data with context information (e.g. study and subject identifiers), respectively. The system is exemplarily demonstrated in a clinical trial, where computer tomography (CT) data is de-centrally captured from the subjects and centrally read by a chief radiologists to decide on inclusion of the subjects in the trial. Errors, latency and costs in the EDC workflow are reduced, while, a research database is implicitly built up in the background.

Today, subject’s medical data in controlled clinical trials is captured digitally in electronic case report forms (eCRFs). However, eCRFs only insufficiently support integration of subject’s image data, although medical imaging is looming large in studies today. For bed-side image integration, we present a mobile application (App) that utilizes the smartphone-integrated camera. To ensure high image quality with this inexpensive consumer hardware, color reference cards are placed in the camera’s field of view next to the lesion. The cards are used for automatic calibration of geometry, color, and contrast. In addition, a personalized code is read from the cards that allows subject identification. For data integration, the App is connected to an communication and image analysis server that also holds the code-study-subject relation. In a second system interconnection, web services are used to connect the smartphone with OpenClinica, an open-source, Food and Drug Administration (FDA)-approved electronic data capture (EDC) system in clinical trials. Once the photographs have been securely stored on the server, they are released automatically from the mobile device. The workflow of the system is demonstrated by an ongoing clinical trial, in which photographic documentation is frequently performed to measure the effect of wound incision management systems. All 205 images, which have been collected in the study so far, have been correctly identified and successfully integrated into the corresponding subject’s eCRF. Using this system, manual steps for the study personnel are reduced, and, therefore, errors, latency and costs decreased. Our approach also increases data security and privacy.

The digital imaging and communications in medicine (DICOM) protocol is the leading standard for image data management in healthcare. Imaging biomarkers and image-based surrogate endpoints in clinical trials and medical registries require DICOM viewer software with advanced functionality for visualization and interfaces for integration. In this paper, a comprehensive evaluation of 28 DICOM viewers is performed. The evaluation criteria are obtained from application scenarios in clinical research rather than patient care. They include (i) platform, (ii) interface, (iii) support, (iv) two-dimensional (2D), and (v) three-dimensional (3D) viewing. On the average, 4.48 and 1.43 of overall 8 2D and 5 3D image viewing criteria are satisfied, respectively. Suitable DICOM interfaces for central viewing in hospitals are provided by GingkoCADx, MIPAV, and OsiriX Lite. The viewers ImageJ, MicroView, MIPAV, and OsiriX Lite offer all included 3D-rendering features for advanced viewing. Interfaces needed for decentral viewing in web-based systems are offered by Oviyam, Weasis, and Xero. Focusing on open source components, MIPAV is the best candidate for 3D imaging as well as DICOM communication. Weasis is superior for workflow optimization in clinical trials. Our evaluation shows that advanced visualization and suitable interfaces can also be found in the open source field and not only in commercial products.

Subject’s medical data in controlled clinical trials is captured in electronic case report forms. We present a mobile application (App) that utilizes the smartphone-integrated camera for integrating photographic documentation directly from subject’s bed-side. Color reference cards are placed next to the wound and used for geometric and contrast registration. This ensures high image quality with the inexpensive consumer hardware. In addition, a code is detected from the card for subject identification. The App connects to an image analysis server and looks up the code-study-subject relation. Then, the smartphone connects with OpenClinica, an open source and electronic data capture system for clinical trials, which has been approved by the US Food and Drug Administration (FDA). The App is demonstrated by an ongoing clinical trial, where wound healing after a vascular surgery is followed up photographically. All 205 images collected in the study so far have been identified and integrated into subject’s eCRF correctly. Avoiding manual mapping of photographs to study subjects avoids errors and latency, decreases costs, and improves data security and privacy.

Medical imaging plays an important role in clinical trials providing qualitative and quantitative findings. Patient's data in studies is captured in electronic case report forms (eCRFs) instead of paper-based CRFs, which are provided by electronic data capture systems (EDCS). However, EDCS insufficiently support integration of image data into patient's eCRF. Neither interfacing with picture archiving and communication systems (PACS), nor managing of digital imaging and communications in medicine (DICOM) data in eCRFs is possible. Hence, manual detours for image data in study's data capture workflow increase error-proneness, latency, and costs. In this work, a completely web-based system architecture is implemented interconnecting EDCS and PACS. Our approach utilizes the open source projects OpenClinica, DCM4CHEE, and Weasis as EDCS, PACS, and DICOM web viewer, respectively. In the optimized workflow, user interaction completely takes place in the eCRF. DICOM data storage and retrieval is performed by middleware components hidden from the user, ensuring data consistency and security by identifier synchronization and de-identification, respectively. This shortens paths for image data capture in the workflow, reduces errors, and saves time and costs. Beside this, valuable data for further research is centrally and anonymously stored in a research PACS.

Photographic documentation in medicine is of increasing importance. Efficient methods are required to properly register and calibrate the images. Usually, a standard reference card with special color pattern is placed in the aperature of the image. Localization and extraction of such cards is a critical step. In this paper, we adopt an iterative lattice detection algorithm developed for outdoor images. Once the lattice is extracted, crossing points of the color fields are used for perspective geometric transform while the color plates guide the color calibration process. Our method is tested on 37 images collected within the German Calciphylaxis Registry. At least, 28 out of the 35 possible grid points have been extracted in all the non-standardizes photographs, with at most two false positive detections. The lowest F-measure was above 80%. Hence, ruler and other calibration devices become obsolete and wound imaging can be performed with low-cost hardware, too.

Photographic documentation in medicine is of increasing importance. Efficient methods are required to properly register and calibrate the images. Usually, a standard reference card with special color pattern is placed in the aperature of the image. Localization and extraction of such cards is a critical step. In this paper, we adopt an iterative lattice detection algorithm developed for outdoor images. Once the lattice is extracted, crossing points of the color fields are used for perspective geometric transform while the color plates guide the color calibration process. Our method is tested on 37 images collected within the German Calciphylaxis Registry. At least, 28 out of the 35 possible grid points have been extracted in all the non-standardizes photographs, with at most two false positive detections. The lowest F-measure was above 80%. Hence, ruler and other calibration devices become obsolete and wound imaging can be performed with low-cost hardware, too.

The digital imaging and communications in medicine (DICOM) protocol is nowadays the leading standard for capture, exchange and storage of image data in medical applications. A broad range of commercial, free, and open source software tools supporting a variety of DICOM functionality exists. However, different from patient's care in hospital, DICOM has not yet arrived in electronic data capture systems (EDCS) for clinical trials. Due to missing integration, even just the visualization of patient's image data in electronic case report forms (eCRFs) is impossible. Four increasing levels for integration of DICOM components into EDCS are conceivable, raising functionality but also demands on interfaces with each level. Hence, in this paper, a comprehensive evaluation of 27 DICOM viewer software projects is performed, investigating viewing functionality as well as interfaces for integration. Concerning general, integration, and viewing requirements the survey involves the criteria (i) license, (ii) support, (iii) platform, (iv) interfaces, (v) two-dimensional (2D) and (vi) three-dimensional (3D) image viewing functionality. Optimal viewers are suggested for applications in clinical trials for 3D imaging, hospital communication, and workflow. Focusing on open source solutions, the viewers ImageJ and MicroView are superior for 3D visualization, whereas GingkoCADx is advantageous for hospital integration. Concerning workflow optimization in multi-centered clinical trials, we suggest the open source viewer Weasis. Covering most use cases, an EDCS and PACS interconnection with Weasis is suggested.

Solving problems in medical image processing is either generic (being applicable to many problems) or specific (optimized for a certain task). For example, bone age assessment (BAA) on hand radiographs is a frequent but cumbersome task for radiologists. For this problem, many specific solutions have been proposed. However, general-purpose feature descriptors are used in many computer vision applications. Hence, the aim of this study is (i) to compare the five leading keypoint descriptors on BAA, and, in doing so, (ii) presenting a generic approach for a specific task. Two methods for keypoint selection were applied: sparse and dense feature points. For each type, SIFT, SURF, BRIEF, BRISK, and FREAK feature descriptors were extracted within the epiphyseal regions of interest (eROI). Classification was performed using a support vector machine. Reference data (1101 radiographs) of the University of Southern California was used for 5-fold cross-validation. The data was grouped into 30 classes representing the bone age range of 0–18 years. With a mean error of 0.605 years, dense SIFT gave best results and outperforms all published methods. The accuracy was 98.36% within the range of 2 years. Dense SIFT represents a generic method for a specific question.

Bone age assessment (BAA) is a method of determining the skeletal maturity and finding the growth disorder in the skeleton of a person. BAA is frequently used in pediatric medicine but also a time-consuming and cumbersome task for a radiologist. Conventionally, the Greulich and Pyle and the Tanner and Whitehouse methods are used for bone age assessment, which are based on visual comparison of left hand radiographs with a standard atlas. We present a novel approach for automated bone age assessment, combining scale invariant feature transform (SIFT) features and support vector machine (SVM) classification. In this approach, (i) data is grouped into 30 classes to represent the age range of 0- 18 years, (ii) 14 epiphyseal ROIs are extracted from left hand radiographs, (iii) multi-level image thresholding, using Otsu method, is applied to specify key points on bone and osseous tissues of eROIs, (iv) SIFT features are extracted for specified key points for each eROI of hand radiograph, and (v) classification is performed using a multi-class extension of SVM. A total of 1101 radiographs of University of Southern California are used in training and testing phases using 5- fold cross-validation. Evaluation is performed for two age ranges (0-18 years and 2-17 years) for comparison with previous work and the commercial product BoneXpert, respectively. Results were improved significantly, where the mean errors of 0.67 years and 0.68 years for the age ranges 0-18 years and 2-17 years, respectively, were obtained. Accuracy of 98.09 %, within the range of two years was achieved.

While medical image data is managed in picture archiving and communication systems (PACS) via the digital imaging and communications in medicine (DICOM) protocol, electronic data capture systems (EDCS) in clinical trials lack PACS interfacing. This complicates the trial workflow and increases errors, time, and costs. In this work, four system architectures of image integration for multi-center trials are analyzed with respect to data, function, visual, and context integration levels. We propose an open source-based architecture composed of OpenClinica, DCM4CHE, and Weasis for EDCS, PACS, and Viewer, respectively.

Providing surrogate endpoints in clinical trials, medical imaging has become increasingly important in human-centered research. Nowadays, electronic data capture systems (EDCS) are used but binary image data is integrated insufficiently. There exists no structured way, neither to manage digital imaging and communications in medicine (DICOM) data in EDCS nor to interconnect EDCS with picture archiving and communication systems (PACS). Manual detours in the trial workflow yield errors, delays, and costs. In this paper, requirements for a DICOM-based system interconnection of EDCS and research PACS are analysed. Several workflow architectures are compared. Optimized for multi-center trials, we propose an entirely web-based solution integrating EDCS, PACS, and DICOM viewer, which has been implemented using the open source projects OpenClinica, DCM4CHEE, and Weasis, respectively. The EDCS forms the primary access point. EDCS to PACS interchange is integrated seamlessly on the data and the context levels. DICOM data is viewed directly from the electronic case report form (eCRF), while PACS-based management is hidden from the user. Data privacy is ensured by automatic de-identification and re-labelling with study identifiers. Our concept is evaluated on a variety of 13 DICOM modalities and transfer syntaxes. We have implemented the system in an ongoing investigator-initiated trial (IIT), where five centers have recruited 24 patients so far, performing decentralized computed tomography (CT) screening. Using our system, the chief radiologist is reading DICOM data directly from the eCRF. Errors and workflow processing time are reduced. Furthermore, an imaging database is built that may support future research.

Image-based surrogates became increasingly important in controlled clinical trials developing novel drugs and medical devices. However, there is as yet no structured way to collect digital imaging and communications in medicine (DICOM) objects in electronic data capture systems (EDCS), i.e., the trial database systems. Recently, van Herk obtained a tight connection of electronic case report forms (eCRFs) and picture archiving and communication systems (PACS) [1]. In this work, we suggest a workflow-based architecture integrating EDCS as the leading component.

Photographic documentation and image-based wound assessment is frequently performed in medical diagnostics, patient care, and clinical research. To support quantitative assessment, photographic imaging is based on expensive and high-quality hardware and still needs appropriate registration and calibration. Using inexpensive consumer hardware such as smartphone-integrated cameras, calibration of geometry, color, and contrast is challenging. Some methods involve color calibration using a reference pattern such as a standard color card, which is located manually in the photographs. In this paper, we adopt the lattice detection algorithm by Park et al. from real world to medicine. At first, the algorithm extracts and clusters feature points according to their local intensity patterns. Groups of similar points are fed into a selection process, which tests for suitability as a lattice grid. The group which describes the largest probability of the meshes of a lattice is selected and from it a template for an initial lattice cell is extracted. Then, a Markov random field is modeled. Using the mean-shift belief propagation, the detection of the 2D lattice is solved iteratively as a spatial tracking problem. Least-squares geometric calibration of projective distortions and non-linear color calibration in RGB space is supported by 35 corner points of 24 color patches, respectively. The method is tested on 37 photographs taken from the German Calciphylaxis registry, where non-standardized photographic documentation is collected nationwide from all contributing trial sites. In all images, the reference card location is correctly identified. At least, 28 out of 35 lattice points were detected, outperforming the SIFT-based approach previously applied. Based on these coordinates, robust geometry and color registration is performed making the photographs comparable for quantitative analysis.

Whole slide imaging (WSI) has become important in medicine and pathology, and challenges processing and management of high-resolution images with up to 10GB of data. Open source tools such as ImageJ do not sufficiently support high volume data and require manual interaction in otherwise automatic workflows. We present an open source ImageJ plugin for automatic processing of Nanozoomer Digital Pathology Images (NDPI). In a batch-orientated workflow, the plugin provides an image processing pipeline including data conversion, segmentation, tiling, region of interest detection, thresholding, and quantification. The plugin is exemplarily applied to quantitative analysis of renal histology images. However, the general design supports other WSI file formats and analysis tasks.

To improve data quality and save cost, clinical trials are nowadays performed using electronic data capture systems (EDCS) providing electronic case report forms (eCRF) instead of paper-based CRFs. However, such EDCS are insufficiently integrated into the medical workflow and lack in interfacing with other study-related systems. In addition, most EDCS are unable to handle image and biosignal data, although electrocardiography (EGC, as example for one-dimensional (1D) data), ultrasound (2D data), or magnetic resonance imaging (3D data) have been established as surrogate endpoints in clinical trials. In this paper, an integrated workflow based on OpenClinica, one of the world's largest EDCS, is presented. Our approach consists of three components for (i) sharing of study metadata, (ii) integration of large volume data into eCRFs, and (iii) automatic image and biosignal analysis. In all components, metadata is transferred between systems using web services and JavaScript, and binary large objects (BLOBs) are sent via the secure file transfer protocol and hypertext transfer protocol. We applied the close-looped workflow in a multicenter study, where long term (7 days/24 h) Holter ECG monitoring is acquired on subjects with diabetes. Study metadata is automatically transferred into OpenClinica, the 4 GB BLOBs are seamlessly integrated into the eCRF, automatically processed, and the results of signal analysis are written back into the eCRF immediately.

Calciphylaxis is a rare disease that has devastating conditions associated with high morbidity and mortality. Calciphylaxis is characterized by systemic medial calcification of the arteries yielding necrotic skin ulcerations. In this paper, we aim at supporting the installation of multi-center registries for calciphylaxis, which includes a photographic documentation of skin necrosis. However, photographs acquired in different centers under different conditions using different equipment and photographers cannot be compared quantitatively. For normalization, we use a simple color pad that is placed into the field of view, segmented from the image, and its color fields are analyzed. In total, 24 colors are printed on that scale. A least-squares approach is used to determine the affine color transform. Furthermore, the card allows scale normalization. We provide a case study for qualitative assessment. In addition, the method is evaluated quantitatively using 10 images of two sets of different captures of the same necrosis. The variability of quantitative measurements based on free hand photography is assessed regarding geometric and color distortions before and after our simple calibration procedure. Using automated image processing, the standard deviation of measurements is significantly reduced. The coefficients of variations yield 5-20% and 2-10% for geometry and color, respectively. Hence, quantitative assessment of calciphylaxis becomes practicable and will impact a better understanding of this rare but fatal disease.

Imaging and image-based measurements nowadays play an essential role in controlled clinical trials, but electronic data capture (EDC) systems insufficiently support integration of captured images by mobile devices (e.g. smartphones and tablets). The web application OpenClinica has established as one of the world's leading EDC systems and is used to collect, manage and store data of clinical trials in electronic case report forms (eCRFs). In this paper, we present a mobile application for instantaneous integration of images into OpenClinica directly during examination on patient's bed site. The communication between the Android application and OpenClinica is based on the simple object access protocol (SOAP) and representational state transfer (REST) web services for metadata, and secure file transfer protocol (SFTP) for image transfer, respectively. OpenClinica's web services are used to query context information (e. g. existing studies, events and subjects) and to import data into the eCRF, as well as export of eCRF metadata and structural information. A stable image transfer is ensured and progress information (e. g. remaining time) visualized to the user. The workflow is demonstrated for a European multi-center registry, where patients with calciphylaxis disease are included. Our approach improves the EDC workflow, saves time, and reduces costs. Furthermore, data privacy is enhanced, since storage of private health data on the imaging devices becomes obsolete.