Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications

• Published in: IEEE transactions on medical imaging 2012-03, Vol.31 (3), p.790-804
• Main Authors: Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G.
• Format: Article
• Language: English
• Subjects: Algorithms; Automata; Biomedical imaging; Brain - anatomy & histology; Brain Neoplasms - pathology; Brain Neoplasms - surgery; Brain tumor segmentation; cellular automata; Clinical medicine; contrast enhanced magnetic resonance imaging (MRI); Databases, Factual; Humans; Image edge detection; Image segmentation; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Models, Biological; necrotic tissue segmentation; Planning; radiosurgery; Radiosurgery - methods; radiotherapy; Radiotherapy, Computer-Assisted - methods; Reproducibility of Results; seeded segmentation; shortest paths; Studies; Tumors
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2011.2181857

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.