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Fast Contextual View Generation in 3D Medical Images Using a 3D Widget User Interface and Super-Ellipsoids

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

This paper presents a 3D widget user interface (UI), super-ellipsoid shape primitives and a customized volume rendering algorithm that together create an effective system for generating contextual views in 3D medical images. The widget UI supports the fast and precise positioning of a super-ellipsoid “paint blob”. The paint blob can be deposited and automatically blended with previously deposited blobs to form an arbitrarily complex-shaped region of interest (ROI) enclosing target image features. The rendering of these “focus” regions can be controlled separately from the surrounding contextual region, allowing medical experts to examine and measure image features relative to the surrounding structures, regardless of the level of occlusion. The system’s core algorithms execute in parallel on graphics processing units, resulting in real-time interaction and high-quality visualizations. The focus plus context visualization system is validated via a user study and a series of experiments.

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Notes

  1. 1.

    Several videos of the tools comprising the technique can be viewed at https://www.youtube.com/watch?v=NsgipyU1lfQ&list=PLtEweT4itM8Ef2D9HLQDAKu6G1Ou1Q3Yr&index=1.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Ryerson University, Toronto, ON, M5B 2K3, Canada

    Ken Lagos & Tim McInerney

Authors
  1. Ken Lagos
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  2. Tim McInerney
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Corresponding author

Correspondence to Tim McInerney .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Lagos, K., McInerney, T. (2019). Fast Contextual View Generation in 3D Medical Images Using a 3D Widget User Interface and Super-Ellipsoids. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_37

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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