Proc. SPIE. 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
KEYWORDS: Medicine, Data storage, Medical imaging, Data acquisition, Data archive systems, Digital imaging, Telecommunications, Computed tomography, Data communications, Computer security, Picture Archiving and Communication System, Clinical trials
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.
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.
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.
Proc. SPIE. 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations
KEYWORDS: Human-machine interfaces, Image visualization, Visualization, Stereoscopy, Medical imaging, Data communications, 3D vision, 3D image processing, Picture Archiving and Communication System, 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.
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.
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.
Bone age assessment on hand radiographs is a frequently and time consuming task to determine growth disturbances in human body. Recently, an automatic processing pipeline, combining content-based image retrieval and support vector regression (SVR), has been developed. This approach was evaluated based on 1,097 radiographs from the University of Southern California. Discretization of SVR continuous prediction to age classes has been done by (i) truncation. In this paper, we apply novel approaches in mapping of SVR continuous output values: (ii) rounding, where 0.5 is added to the values before truncation; (iii) curve, where a linear mapping curve is applied between the age classes, and (iv) age, where artificial age classes are not used at all. We evaluate these methods on the age range of 0-18 years, and 2-17 years for comparison with the commercial product BoneXpert that is using an active shape approach. Our methods reach root-mean-square (RMS) errors of 0.80, 0.76 and 0.73 years, respectively, which is slightly below the performance of the BoneXpert.