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.
- Text Instructions
- Ferret Brain
- Contour Drawing
- Expert Segmentation
- Contour Parallel
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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The authors would like to thank Dr. Daniel Low, formerly of the Washington University School of Medicine in St. Louis for the liver data sets, Dr. Sandra Rugonyi of Oregon Health & Science University for the aorta data sets, and Dr. Philip Bayly of Washington University in St. Louis for the ferret brain data sets.
This work was supported by the National Science Foundation under grants DEB-1053554 and IIS-1302142.
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Holloway, M. et al. (2015). Guided Structure-Aligned Segmentation of Volumetric Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27856-8
Online ISBN: 978-3-319-27857-5