Daniel Haak

Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment

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.

DICOM for Clinical Research: PACS-Integrated Electronic Data Capture in Multi-Center Trials.

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.

A Survey of DICOM Viewer Software to Integrate Clinical Research and Medical Imaging

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.

Evaluation of DICOM viewer software for workflow integration in clinical trials

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.

Integrated Image Data and Medical Record Management for Rare Disease Registries. A General Framework and its Instantiation to the German Calciphylaxis Registry

Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1–7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future.

Digital Imaging and Electronic Data Capture in Multi-Center Clinical Trials

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.

Bone age assessment meets SIFT

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.

Integrating image management and analysis into OpenClinica using web services

Although image-based measures have become an important surrogate for primary endpoints in controlled clinical trials, electronic data capture (EDC) insufficiently supports image and signal data files. In this paper, we suggest a simple extension of OpenClinica, the world’s largest open source EDC system, to handle image data files, process image and signal data, and fill out the electronic case report forms (eCRF) accordingly. We use the web service server interface that is integrated with OpenClinica. The missing client component is substituted by CRF embedded JavaScript and a PHP proxy on server side. JavaScript is also used to display images within the OpenClinica interface. The counterpart system was developed using the Google Web Toolkit (GWT) and the Java application programming interface (API) for eXtensible Markup Language (XML) web services (JAX-WS). Image processing is implemented in Java using ImageJ libraries. We demonstrate the workflow for CRFs of a conjunctival provocation test, where two photographs of a human eye are captured, transferred into the eCRF, segmented and measured. The secure file transfer protocol (SFTP) is used to transfer the data files between the systems, and web services are used to fill the eCRFs, which also integrate resulting images generated by the analysis process. Both, images as well as computed measures are automatically displayed within the OpenClinica eCRFs and can be evaluated by the study nurse after file upload. This allows re-capturing of images in case of evaluation failure, and avoids elaborative query management. In future, DICOM-based data transfer will be implemented.

OC ToGo: bed site image integration into OpenClinica with mobile devices

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.

Towards quantitative assessment of calciphylaxis

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.