Improvements in education in pathology: virtual 3D specimens.
ABSTRACT 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 (http://patho.med.uni-magdeburg.de/research.shtml) 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.
- Histopathology 12/2011; 59(6):1263-6. · 3.30 Impact Factor
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ABSTRACT: This paper discusses a pilot study in collaboration between the Centre for Anatomy and Human Identification and the Pathology Department at Ninewells Hospital, Dundee. Anonymised patient MRI data depicting renal cancer was used to create a virtual 3D model and two rapid prototype models of the kidneys and surrounding anatomy. A questionnaire was conducted to collect feedback from tutors and students in order to evaluate the models and determine user preference. It was found that the majority preferred the physical models to the virtual model.Journal of Visual Communication in Medicine 06/2013; 36(1-2):11-9.
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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 http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (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. 01/2011; 1(1):18.