%0 Journal Article %J Sci Rep %D 2018 %T Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. %A Vandaele, Rémy %A Aceto, Jessica %A Muller, Marc %A Péronnet, Frédérique %A Debat, Vincent %A Wang, Ching-Wei %A Huang, Cheng-Ta %A Jodogne, Sébastien %A Martinive, Philippe %A Geurts, Pierre %A Marée, Raphaël %K Algorithms %K Animals %K Body Weights and Measures %K Drosophila %K Humans %K Image Processing, Computer-Assisted %K Software %K Zebrafish %X

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.

%B Sci Rep %V 8 %P 538 %8 2018 01 11 %G eng %N 1 %R 10.1038/s41598-017-18993-5 %0 Journal Article %J Sci Rep %D 2017 %T QuPath: Open source software for digital pathology image analysis. %A Bankhead, Peter %A Loughrey, Maurice B %A Fernández, José A %A Dombrowski, Yvonne %A McArt, Darragh G %A Dunne, Philip D %A McQuaid, Stephen %A Gray, Ronan T %A Murray, Liam J %A Coleman, Helen G %A James, Jacqueline A %A Salto-Tellez, Manuel %A Hamilton, Peter W %K Algorithms %K Biomarkers, Tumor %K Colonic Neoplasms %K Humans %K Image Interpretation, Computer-Assisted %K Kaplan-Meier Estimate %K Programmed Cell Death 1 Ligand 2 Protein %K User-Computer Interface %X

QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath's flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.

%B Sci Rep %V 7 %P 16878 %8 2017 12 04 %G eng %N 1 %R 10.1038/s41598-017-17204-5 %0 Journal Article %J Can Fam Physician %D 2015 %T Identifying patients with asthma in primary care electronic medical record systems Chart analysis-based electronic algorithm validation study. %A Xi, Nancy %A Wallace, Rebecca %A Agarwal, Gina %A Chan, David %A Gershon, Andrea %A Gupta, Samir %K Adult %K Aged %K Algorithms %K Asthma %K Data Accuracy %K electronic health records %K Female %K Humans %K Male %K Middle Aged %K Ontario %K Primary Health Care %K Pulmonary Disease, Chronic Obstructive %K Registries %K Retrospective Studies %K Sensitivity and Specificity %X

OBJECTIVE: To develop and test a variety of electronic medical record (EMR) search algorithms to allow clinicians to accurately identify their patients with asthma in order to enable improved care.

DESIGN: A retrospective chart analysis identified 5 relevant unique EMR information fields (electronic disease registry, cumulative patient profile, billing diagnostic code, medications, and chart notes); asthma-related search terms were designated for each field. The accuracy of each term was tested for its ability to identify the asthma patients among all patients whose charts were reviewed. Increasingly sophisticated search algorithms were then designed and evaluated by serially combining individual searches with Boolean operators.

SETTING: Two large academic primary care clinics in Hamilton, Ont.

PARTICIPANTS: Charts for 600 randomly selected patients aged 16 years and older identified in an initial EMR search as likely having asthma (n = 150), chronic obstructive pulmonary disease (n = 150), other respiratory conditions (n = 150), or nonrespiratory conditions (n = 150) were reviewed until 100 patients per category were identified (or until all available names were exhausted). A total of 398 charts were reviewed in full and included.

MAIN OUTCOME MEASURES: Sensitivity and specificity of each search for asthma diagnosis (against the reference standard of a physician chart review-based diagnosis).

RESULTS: Two physicians reviewed the charts identified in the initial EMR search using a standardized data collection form and ascribed the following diagnoses in 398 patients: 112 (28.1%) had asthma, 81 (20.4%) had chronic obstructive pulmonary disease, 104 (26.1%) had other respiratory conditions, and 101 (25.4%) had nonrespiratory conditions. Concordance between reviewers in chart abstraction diagnosis was high (κ = 0.89, 95% CI 0.80 to 0.97). Overall, the algorithm searching for patients who had asthma in their cumulative patient profiles or for whom an asthma billing code had been used was the most accurate (sensitivity of 90.2%, 95% CI 87.3% to 93.1%; specificity of 83.9%, 95% CI 80.3% to 87.5%).

CONCLUSION: Usable, practical search algorithms that accurately identify patients with asthma in existing EMRs are presented. Clinicians can apply 1 of these algorithms to generate asthma registries for targeted quality improvement initiatives and outcome measurements. This methodology can be emulated for other diseases.

%B Can Fam Physician %V 61 %P e474-83 %8 2015 Oct %G eng %N 10 %0 Journal Article %J J Digit Imaging %D 2014 %T Simplifying electronic data capture in clinical trials: workflow embedded image and biosignal file integration and analysis via web services. %A Haak, Daniel %A Samsel, Christian %A Gehlen, Johan %A Jonas, Stephan %A Deserno, Thomas M %K Algorithms %K Automatic Data Processing %K Clinical Trials as Topic %K Database Management Systems %K Humans %K Image Processing, Computer-Assisted %K Information Storage and Retrieval %K Internet %K Medical Records Systems, Computerized %K Systems Integration %K Workflow %X

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.

%B J Digit Imaging %V 27 %P 571-80 %8 2014 Oct %G eng %N 5 %R 10.1007/s10278-014-9694-z %0 Journal Article %J Phys Med Biol %D 2012 %T STIR: software for tomographic image reconstruction release 2. %A Thielemans, Kris %A Tsoumpas, Charalampos %A Mustafovic, Sanida %A Beisel, Tobias %A Aguiar, Pablo %A Dikaios, Nikolaos %A Jacobson, Matthew W %K Algorithms %K Animals %K Computers %K Image Processing, Computer-Assisted %K Mice %K Software %K Tomography %X We present a new version of STIR (Software for Tomographic Image Reconstruction), an open source object-oriented library implemented in C++ for 3D positron emission tomography reconstruction. This library has been designed such that it can be used for many algorithms and scanner geometries, while being portable to various computing platforms. This second release enhances its flexibility and modular design and includes additional features such as Compton scatter simulation, an additional iterative reconstruction algorithm and parametric image reconstruction (both indirect and direct). We discuss the new features in this release and present example results. STIR can be downloaded from http://stir.sourceforge.net. %B Phys Med Biol %V 57 %P 867-83 %8 2012 Feb 21 %G eng %N 4 %R 10.1088/0031-9155/57/4/867 %0 Journal Article %J AMIA ... Annual Symposium proceedings. AMIA Symposium %D 2011 %T The {SHARPn} project on secondary use of {Electronic} {Medical} {Record} data: progress, plans, and possibilities %A Chute, Christopher G. %A Pathak, Jyotishman %A Savova, Guergana K. %A Bailey, Kent R. %A Schor, Marshall I. %A Hart, Lacey A. %A Beebe, Calvin E. %A Huff, Stanley M. %K Algorithms %K Biomedical Research %K Cooperative Behavior %K Data Mining %K electronic health records %K Natural Language Processing %K Software %X SHARPn is a collaboration among 16 academic and industry partners committed to the production and distribution of high-quality software artifacts that support the secondary use of EMR data. Areas of emphasis are data normalization, natural language processing, high-throughput phenotyping, and data quality metrics. Our work avails the industrial scalability afforded by the Unstructured Information Management Architecture (UIMA) from IBM Watson Research labs, the same framework which underpins the Watson Jeopardy demonstration. This descriptive paper outlines our present work and achievements, and presages our trajectory for the remainder of the funding period. The project is one of the four Strategic Health IT Advanced Research Projects (SHARP) projects funded by the Office of the National Coordinator in 2010. %B AMIA ... Annual Symposium proceedings. AMIA Symposium %V 2011 %P 248–256 %G eng %0 Journal Article %J BMC Med Imaging %D 2010 %T An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging. %A Messroghli, Daniel R %A Rudolph, Andre %A Abdel-Aty, Hassan %A Wassmuth, Ralf %A Kuhne, Titus %A Dietz, Rainer %A Schulz-Menger, Jeanette %K Algorithms %K Humans %K Image Enhancement %K Image Interpretation, Computer-Assisted %K Magnetic Resonance Imaging %K Programming Languages %K Reproducibility of Results %K Sensitivity and Specificity %K Software %X

BACKGROUND: In magnetic resonance (MR) imaging, T1, T2 and T2* relaxation times represent characteristic tissue properties that can be quantified with the help of specific imaging strategies. While there are basic software tools for specific pulse sequences, until now there is no universal software program available to automate pixel-wise mapping of relaxation times from various types of images or MR systems. Such a software program would allow researchers to test and compare new imaging strategies and thus would significantly facilitate research in the area of quantitative tissue characterization.

RESULTS: After defining requirements for a universal MR mapping tool, a software program named MRmap was created using a high-level graphics language. Additional features include a manual registration tool for source images with motion artifacts and a tabular DICOM viewer to examine pulse sequence parameters. MRmap was successfully tested on three different computer platforms with image data from three different MR system manufacturers and five different sorts of pulse sequences: multi-image inversion recovery T1; Look-Locker/TOMROP T1; modified Look-Locker (MOLLI) T1; single-echo T2/T2*; and multi-echo T2/T2*. Computing times varied between 2 and 113 seconds. Estimates of relaxation times compared favorably to those obtained from non-automated curve fitting. Completed maps were exported in DICOM format and could be read in standard software packages used for analysis of clinical and research MR data.

CONCLUSIONS: MRmap is a flexible cross-platform research tool that enables accurate mapping of relaxation times from various pulse sequences. The software allows researchers to optimize quantitative MR strategies in a manufacturer-independent fashion. The program and its source code were made available as open-source software on the internet.

%B BMC Med Imaging %V 10 %P 16 %8 2010 %G eng %R 10.1186/1471-2342-10-16 %0 Journal Article %J BMC Bioinformatics %D 2009 %T Metadata mapping and reuse in caBIG. %A Kunz, Isaac %A Lin, Ming-Chin %A Frey, Lewis %K Algorithms %K Computational Biology %K Database Management Systems %K Databases, Factual %K Medical Informatics %K Software %K User-Computer Interface %X

BACKGROUND: This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI)'s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications. This effort is intended to help developers reuse appropriate CDEs to enable interoperability of their systems when developing within the caBIG framework or other frameworks that use metadata repositories.

RESULTS: The Dice (di-grams) and Dynamic algorithms are compared and both algorithms have similar performance matching UML model class-attributes to CDE class object-property pairs. With algorithms used, the baselines for automatically finding the matches are reasonable for the data models examined. It suggests that automatic mapping of UML models and CDEs is feasible within the caBIG framework and potentially any framework that uses a metadata repository.

CONCLUSION: This work opens up the possibility of using mapping algorithms to reduce cost and time required to map local data models to a reference data model such as those used within caBIG. This effort contributes to facilitating the development of interoperable systems within caBIG as well as other metadata frameworks. Such efforts are critical to address the need to develop systems to handle enormous amounts of diverse data that can be leveraged from new biomedical methodologies.

%B BMC Bioinformatics %V 10 Suppl 2 %P S4 %8 2009 %G eng %R 10.1186/1471-2105-10-S2-S4 %0 Journal Article %J Medical image analysis %D 2005 %T The medical imaging interaction toolkit. %A Wolf, Ivo %A Vetter, Marcus %A Wegner, Ingmar %A Böttger, Thomas %A Nolden, Marco %A Schöbinger, Max %A Hastenteufel, Mark %A Kunert, Tobias %A Meinzer, Hans-Peter %K Algorithms %K Artificial Intelligence %K Computer Graphics %K Diagnostic Imaging %K Image Enhancement %K Image Interpretation, Computer-Assisted %K Imaging, Three-Dimensional %K Pattern Recognition, Automated %K Software %K User-Computer Interface %X Thoroughly designed, open-source toolkits emerge to boost progress in medical imaging. The Insight Toolkit (ITK) provides this for the algorithmic scope of medical imaging, especially for segmentation and registration. But medical imaging algorithms have to be clinically applied to be useful, which additionally requires visualization and interaction. The Visualization Toolkit (VTK) has powerful visualization capabilities, but only low-level support for interaction. In this paper, we present the Medical Imaging Interaction Toolkit (MITK). The goal of MITK is to significantly reduce the effort required to construct specifically tailored, interactive applications for medical image analysis. MITK allows an easy combination of algorithms developed by ITK with visualizations created by VTK and extends these two toolkits with those features, which are outside the scope of both. MITK adds support for complex interactions with multiple states as well as undo-capabilities, a very important prerequisite for convenient user interfaces. Furthermore, MITK facilitates the realization of multiple, different views of the same data (as a multiplanar reconstruction and a 3D rendering) and supports the visualization of 3D+t data, whereas VTK is only designed to create one kind of view of 2D or 3D data. MITK reuses virtually everything from ITK and VTK. Thus, it is not at all a competitor to ITK or VTK, but an extension, which eases the combination of both and adds the features required for interactive, convenient to use medical imaging software. MITK is an open-source project (www.mitk.org). %B Medical image analysis %V 9 %P 594-604 %8 2005 Dec %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/15896995?dopt=Abstract