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| Title | Visualization for Validation and Improvement of Three-dimensional Segmentation Algorithms
(In Proceedings) |
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| in | Data Visualization 2005 (Proceedings of the EUROGRAPHICS - IEEE VGTC Symposium on Visualization 2005) |
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| Author(s) |
Gunther H. Weber, Cristian Luis Hendriks Luengo, Soile V. E. Keränen, Scott E. Dillard, Derek Ju, Damir Sudar, Bernd Hamann |
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| Editor(s) |
Ken Brodlie, David Duke, Ken Joy |
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| Keyword(s) | image processing, volume visualization, gene expression |
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| Year |
June 2005
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| Location | Leeds, United Kingdom |
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| Date | June 1--3, 2005 |
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| Publisher | Eurographics Association |
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| Address | PO Box 16, CH-1288 Aire-la-Ville, Switzerland |
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| Organization | EUROGRAPHICS - IEEE VGTC |
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| Abstract |
The Berkeley Drosophila Transcription Network Project (BDTNP) is developing a suite
of methods that will allow a quantitative description and analysis of three
dimensional (3D) gene expression patterns in an animal with cellular resolution. An
important component of this approach are algorithms that segment 3D images of an
organism into individual nuclei and cells and measure relative levels of gene
expression. As part of the BDTNP, we are developing tools for interactive
visualization, control, and verification of these algorithms. Here we present a
volume visualization prototype system that, combined with user interaction tools,
supports validation and quantitative determination of the accuracy of nuclear
segmentation. Visualizations of nuclei are combined with information obtained from a
nuclear segmentation mask, supporting the comparison of raw data and its
segmentation. It is possible to select individual nuclei interactively in a volume
rendered image and identify incorrectly segmented objects. Integration with segmentation
algorithms, implemented in MATLAB, makes it possible to modify a segmentation based
on visual examination and obtain additional information about incorrectly segmented
objects. This work has already led to significant improvements in segmentation
accuracy and opens the way to enhanced analysis of images of complex animal
morphologies.
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