Abstract
In the last decades several liver segmentation methods have been proposed. The proposed methods go from region growing to the more complex statistical shape models. Despite the robustness of those algorithms, liver segmentation is still a challenging task especially in areas in which its neighboring organs have similar intensities, e.g., heart and ribcage. In addition to this, pathological organs that contain tumors near their surface present additional difficulties. This paper presents a solution to increase the accuracy of those algorithms in the aforementioned areas. The effect of the improvement using the generated heart and ribcage walls (7% and 1% respectively) is evaluated on 9 clinical computer tomographies (CT). The improvement (12 %) when tumors are near the surface, on the contrary, is tested on 7 clinical CT images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Peng J, Dong F, Chen Y et al. A region-appearance-based adaptive variational model for 3D liver segmentation Med. Phys.. 2014;41.
Gariau R, Cuingnet R, Prevost R et al. A generic, robust and fully-automatic workflow for 3D CT liver segmentation Abdominal Imaging. 2013:241 - 250.
Narkbuakaew W, Nagahashi H, Aoki K et al. An efficient liver-segmentation system based on a level-set method and consequent processes J. Biomedical Science and Engineering. 2014;7:994-1004.
Maier F, Wimmer A, Soza G et al. Automatic liver segmentation using the random walker algorithm in BVM:56-61 2008.
Chi Y, Cashman P M M, Bello F et al. A discussion on the evaluation of a new automatic liver volume segmentation method for specified CT image datasets in Workshop on 3D segmentation in the clinic:167-175 2007.
Ruskó L, Bekes G, Fidrich M. Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images Med. Image Anal.. 2009;13:871-882.
Deriche R. Fast Algorithms for Low-Level Vision IEEE Trans. Pattern Anal. Mach. Intell.. 1990;12:78-87.
Lindeberg T. Discrete Scale-Space Theory and the Scale-Space Primal Sketch. PhD thesisRoyal Institute of Technology 1991.
Hoppe H. Surface reconstruction from unorganized points. PhD thesisUniversity of Washington 1994.
Erdt M, Steger S, Kirschner M et al. Fast Automatic Liver Segmentation Combining Learned Shape Priors with Observed Shape Deviation in IEEE Symposium on CBMS:249-254 2010.
Oyarzun Laura C, Drechsler K, Wesarg S. Anatomical Discovery: Finding Organs in the Neighborhood of the Liver in MEDICON;41:348-351 2014.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Laura, C.O., Oelmann, S., Drechsler, K., Wesarg, S. (2016). Reducing Over- and Undersegmentations of the Liver in Computed Tomographies Using Anatomical Knowledge. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_75
Download citation
DOI: https://doi.org/10.1007/978-3-319-32703-7_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32701-3
Online ISBN: 978-3-319-32703-7
eBook Packages: EngineeringEngineering (R0)