Abstract
Computer–based medical image analysis is often initialized with the localization of anatomical structures in clinical scans. Many methods have been proposed for segmenting single and multiple anatomical structures. However, it is uncommon to compare different approaches with the same test set, namely a publicly available one. The comparison of these methods objectively defines the advantages and limitations for each method. A hierarchic multi–atlas based segmentation approach was proposed for the segmentation of multiple anatomical structures in computed tomography scans. The method relies on an anatomical hierarchy that exploits the inherent spatial and anatomical variability of medical images using image registration techniques. It was submitted and tested in the VISCERAL project Anatomy benchmarks. In this paper, the results are analyzed and compared to the results of the other segmentation methods submitted in the benchmark. Various anatomical structures in both un–enhanced and contrast–enhanced CT scans resulted in the highest overlap with the proposed method compared to the other evaluated approaches. Although the method was trained with a small training set it generated accurate output segmentations for liver, lungs and other anatomical structures.
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Notes
- 1.
http://www.visceral.eu/, as of 5 October 2014.
- 2.
Elastix:http://elastix.isi.uu.nl, 2014. [Online; accesed 5–October–2014].
- 3.
http://www.visceral.eu/closed-benchmarks/benchmark-1/benchmark-1-results/, as of 5 October 2014.
- 4.
http://www.visceral.eu/closed-benchmarks/anatomy2/anatomy2-results/, as of 5 October 2014.
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Acknowledgments
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2014) under grant agreement \(\mathrm{n}^{\circ }\ 318068\) VISCERAL.
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Jiménez del Toro, O.A., Müller, H. (2014). Hierarchic Multi–atlas Based Segmentation for Anatomical Structures: Evaluation in the VISCERAL Anatomy Benchmarks. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_18
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DOI: https://doi.org/10.1007/978-3-319-13972-2_18
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