Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications

Hamamcı, Andaç and Küçük, Nadir and Karaman, Kutlay and Engin, Kayıhan and Ünal, Gözde (2012) Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Transactions on Medical Imaging, 31 (3). pp. 790-804. ISSN 0278-0062

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In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.
Item Type: Article
Uncontrolled Keywords: Brain tumor segmentation; cellular automata; contrast enhanced magnetic resonance imaging (MRI); necrotic tissue segmentation; radiosurgery; radiotherapy; seeded segmentation; shortest paths
Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Gözde Ünal
Date Deposited: 20 Apr 2012 10:48
Last Modified: 31 Jul 2019 10:50
URI: https://research.sabanciuniv.edu/id/eprint/18998

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