Improvements in education in pathology: Virtual 3D specimens

Department of Pathology, Otto-von-Guericke-University, Leipziger Str. 44, D-39120 Magdeburg, Germany.
Pathology - Research and Practice (Impact Factor: 1.4). 06/2009; 205(12):811-4. DOI: 10.1016/j.prp.2009.04.011
Source: PubMed


Virtual three-dimensional (3D) specimens correspond to 3D visualizations of real pathological specimens on a computer display. We describe a simple method for the digitalization of such specimens from high-quality digital images. The images were taken during a whole rotation of a specimen, and merged together into a JPEG2000 multi-document file. The files were made available in the internet ( and obtained very positive ratings by medical students. Virtual 3D specimens expand the application of digital techniques in pathology, and will contribute significantly to the successful introduction of knowledge databases and electronic learning platforms.

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    • "Digital images are increasingly being used in the field of cytopathology for tele-education, clinical consultation, telecytology , remote conferences, web-based learning, quality assurance, and secondary applications such as image analysis [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. A digital image is represented in a computer by a two-dimensional array of numbers, each element of which represents a pixel (picture element). "
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    ABSTRACT: Rapid advances are occurring in the field of cytopathology, particularly in the field of digital imaging. Today, digital images are used in a variety of settings including education (E-education), as a substitute to multiheaded sessions, multisite conferences, publications, cytopathology web pages, cytology proficiency testing, telecytology, consultation through telecytology, and automated screening of Pap test slides. The accessibility provided by digital imaging in cytopathology can improve the quality and efficiency of cytopathology services, primarily by getting the expert cytopathologist to remotely look at the slide. This improved accessibility saves time and alleviates the need to ship slides, wait for glass slides, or transport pathologists. Whole slide imaging (WSI) is a digital imaging modality that uses computerized technology to scan and convert pathology and cytology glass slides into digital images (digital slides) that can be viewed remotely on a workstation using viewing software. In spite of the many advances, challenges remain such as the expensive initial set-up costs, workflow interruption, length of time to scan whole slides, large storage size for WSI, bandwidth restrictions, undefined legal implications, professional reluctance, and lack of standardization in the imaging process.
    Pathology Research International 07/2011; 2011(11):264683. DOI:10.4061/2011/264683
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    • "The Java-based remote viewing station JaRViS was an early example of a medical image viewing and report generating tool that exploited local-area network systems for web-based image processing of diagnostic images generated through nuclear medicine [13]. Kalinski et al. introduced virtual 3D microscopy using JPEG2000 for the visualization of pathology specimens in the Digital Imaging and Communications in Medicine (DICOM) format to create a knowledge database and online learning platforms [25]. Kim et al. proposed the Functional Imaging Web (FIWeb) [26]. "
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    ABSTRACT: Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link (user name: tviangte and password: demome) Our proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered.
    Journal of Clinical Bioinformatics 07/2011; 1(1):18. DOI:10.1186/2043-9113-1-18

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