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titleCellular Automata Segmentation of Brain Tumors on Post Contrast MR Images
authorsA. Hamamcı, G. Unal, K. Engin, N. Kucuk
keywordsSegmentation, Brain Tumors, T1 Weighted MRI
abstractA quantitative analysis of brain tumors is an important factor that can have direct impact on a patient’s prognosis and treatment. In order to achieve clinical relevance, reproducibility and especially accuracy of a proposed method have to be tested. We propose a framework for the generation of realistic digital phantoms of brain tumors of known volumes and their incorporation into an MR dataset of a healthy volunteer. Deformations that occur due to tumor growth inside the brain are simulated by means of a biomechanical model. Furthermore, a model for the amount of edema at each voxel is included as well as a simulation of contrast enhancement, which provides us with an additional characterization of the tumor. A “ground truth” is generally not available for brain tumors. Our proposed framework provides a flexible tool to generate representative datasets with known ground truth, which is essential for the validation and comparison of current and new quantitative approaches. Experiments are carried out using a semi-automated volumetry approach for a set of generated tumor datasets.
typeConference Paper
journalMICCAI 2010
published year2010
(Total records:1429)
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