International Symposium on Visual Computing

Advances in Visual Computing pp 307-317 | Cite as

Guided Structure-Aligned Segmentation of Volumetric Data

  • Michelle Holloway
  • Anahita Sanandaji
  • Deniece Yates
  • Amali Krigger
  • Ross Sowell
  • Ruth West
  • Cindy Grimm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)

Abstract

Segmentation of volumetric images is considered a time and resource intensive bottleneck in scientific endeavors. Automatic methods are becoming more reliable, but many data sets still require manual intervention. Key difficulties include navigating the 3D image, determining where to place marks, and maintaining consistency between marks and segmentations. Clinical practice often requires segmenting many different instances of a specific structure. In this research we leverage the similarity of a repeated segmentation task to address these difficulties and reduce the cognitive load for segmenting on non-traditional planes. We propose the idea of guided contouring protocols that provide guidance in the form of an automatic navigation path to arbitrary cross sections, example marks from similar data sets, and text instructions. We present a user study that shows the usability of this system with non-expert users in terms of segmentation accuracy, consistency, and efficiency.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michelle Holloway
    • 1
  • Anahita Sanandaji
    • 2
  • Deniece Yates
    • 2
  • Amali Krigger
    • 5
  • Ross Sowell
    • 4
  • Ruth West
    • 3
  • Cindy Grimm
    • 2
  1. 1.Washington University in St. LouisSt. LouisUSA
  2. 2.Oregon State UniversityCorvallisUSA
  3. 3.University of North TexasDentonUSA
  4. 4.Cornell CollegeVernonUSA
  5. 5.University of the Virgin IslandsCharlotte AmalieUSA

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