Abstract
The Visceral project organizes a benchmark on multiple anatomical structure segmentation. A training set is provided to the participants that includes a sample of the manual annotations of these structures. To evaluate different segmentation approaches a testing set of volumes must be segmented automatically in a limited period of time. A multi-atlas based segmentation approach is proposed. This technique can be implemented automatically and applied to different anatomical structures with a large enough training set. The addition of a hierarchical local alignment based on anatomical knowledge and local contrast is explained in the approach. An initial experiment to evaluate the impact of using a local alignment and its results show a higher overlap (\({>}9.7\,\%\)) of the structures measured with the Jaccard coefficient. The approach is an effective and easy to implement method that adjusts well to the Visceral benchmark.
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Notes
- 1.
http://www.visceral.eu, as of 14 September 2013.
- 2.
http://www.visceral.eu, as of 14 September 2013.
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Acknowledgments
This work was supported by the EU/FP7 through VISCERAL (318068).
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Jiménez del Toro, Ó.A., Müller, H. (2014). Multi-structure Atlas-Based Segmentation Using Anatomical Regions of Interest. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_21
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DOI: https://doi.org/10.1007/978-3-319-05530-5_21
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