AnatomyBrowser: A novel approach to visualization and integration of medical information

Golland, P.; Kikinis, R.; Umans, C.; Grimson, W.E.L.; Shenton, M.E.; Richolt, J.A.

doi:10.1002/(sici)1097-0150(1999)4:3<129::aid-igs2>3.3.co;2-9

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…Indeed, the main difference among the existing methods is the trade-off between flexibility and meaningful. Previous work [52,53] associates virtual body models, generated from the Visible Human dataset, with a knowledge base of descriptive information (symbolic), which permits an intuitive method for anatomy training in a distributed environment. However, semantic annotations have potentiality to go beyond simply portraying the anatomy for training and can bridge the ambiguity of the natural language by expressing notions and their computational representation in a formal language.…”

Section: Integration Between Visual and Symbolic Representationmentioning

confidence: 99%

Combination of visual and symbolic knowledge: A survey in anatomy

Banerjee

Patan

Spagnuolo

2017

Computers in Biology and Medicine

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Section: Integration Between Visual and Symbolic Representationmentioning

confidence: 99%

Combination of visual and symbolic knowledge: A survey in anatomy

Banerjee

Patan

Spagnuolo

2017

Computers in Biology and Medicine

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“…Most of atlases use predefined images and animations, and offer limited visualization capabilities. Those limitations are overcome by constructing 3D brain atlases based on radiological images or Visible Human data, such as VOXEL-MAN, Anatomy Browser [30] and others listed above. The limitations of these 3D atlases include lack of registration capabilities and a coarse anatomical parcellation of cerebral structures.…”

Section: Brain Atlas Comparisonmentioning

confidence: 99%

From research to clinical practice: lessons learnt from the Cerefy brain atlases

Nowinski

2007

Int J CARS

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Purpose Experience in development and commercialization of the Cerefy brain atlases is shared and compared with some other neuroimaging resources. Materials and Methods Cerefy includes four groups of atlases such as anatomical, functional, vascular and blood supply territories. This project developed 17 brain atlas products being used worldwide in clinical practice, medical research, and education. These atlases are incorporated in more than 1,000 neurosurgical workstations and distributed on over 4,000 CD-ROMs to individual clinicians and medical schools. These brain atlas products were compared with several academic and market performance indicators. Results Cerefy resulted in 13 patents filed and 3 granted, 44 journal papers, 10 international awards, and revenue from commercial products. Numerous anonymous patients have been benefited directly or indirectly from this work, but the impact on surgical practice is more difficult to assess. Conclusion The experience and the lessons learned in the Cerefy brain atlas development were identified, so this can guide other research groups in their translational efforts from bench to bedside.

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“…Although an exhaustive number of image viewing and editing tools have been developed [6,7,8,9], most existing visualization packages have limited manual editing tools and are not designed to integrate automated segmentation algorithms. In this paper, we present a new, integrated segmentation and visualization tool, called the LONI Anatomist, which provides a framework for applying automated segmentation algorithms and interactive tools to manually edit the automated segmentation results.…”

Section: Introductionmentioning

confidence: 99%

An integrated segmentation and visualization tool for MR brain image processing

Zhuang

Valentino

Stambolstian

et al. 2007

Medical Imaging 2007: Visualization and Image-Guided Procedures

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The automated segmentation of brain structures is an important step in many neuroimaging analyses. A variety of automated segmentation tools exist, however, most segmentation results are imperfect, and require manual editing of the resulting contours or surfaces. A new, integrated segmentation and visualization tool, the LONI Anatomist, was developed to provide an open architecture for applying automated segmentation algorithms and interactive tools to manually edit the automated segmentation results. Two automated segmentation algorithms were developed to skullstrip MR brain images and were integrated in the LONI Anatomist: a two-dimensional model-based level set (2D MLS) algorithm and a three-dimensional MLS algorithm. These MLS algorithms were based on the Level Set methods by incorporating two constraints into the level set framework to evolve the zero level set surface in 2D space and 3D space respectively. In the LONI Anatomist, the evolution of the level set was displayed in real time, and final results were corrected using easy-to-use interactive editing tools. Additional tools were provided to visualize the results, such as color overlays of 2D contours over the original gray-scale slices, 3D surface visualization, etc. The LONI Anatomist was implemented in Java using a portable imaging framework (the jViewbox) for medical image display and manipulation, using the Java Image I/O plug-ins for reading/writing DICOM, MINC, ANALYZE image files, and using the Java Advanced Imaging classes for image processing. The design of the system provides a framework for researchers to integrate more mathematical algorithms for converting the algorithms into practical use.

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AnatomyBrowser: A novel approach to visualization and integration of medical information

Cited by 7 publications

References 5 publications

Combination of visual and symbolic knowledge: A survey in anatomy

Combination of visual and symbolic knowledge: A survey in anatomy

From research to clinical practice: lessons learnt from the Cerefy brain atlases

An integrated segmentation and visualization tool for MR brain image processing

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