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

By A. Hamamci, N. Kucuk, K. Karaman, K. Engin, Gözde B. Ünal. IEEE Transactions on Medical Imaging, 2012

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Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network (2018, methodology): “[6] Synthetic data from Utah + in vivo data from Harvard A cellular automata method combining a probability framework 0.”
Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods (2016, methodology): “Hamamci et al. proposed the “Tumor Cut” method 9 .”
A survey of MRI-based brain tumor segmentation methods (2014, methodology): “A tumor-cut algorithm was introduced [127] which combines the tumor segmentation using cellular automata with a level set evolving on the tumor probability map to impose spatial smoothness.”
A survey of MRI-based medical image analysis for brain tumor studies (2013, methodology): “Hamamci et al. (2011) introduced the tumor-cut algorithm, which combines tumor segmentation using cellular automata with a level-set evolving on the tumor probability map to impose spatial smoothness. Neighborhood relationships are often used for spatial regularization after initial voxel-wise class”
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “Algorithm and Data As described in detail in the “Tumor-cut” article [72], the semi-automatic tumor segmentation method by Hamamci and Unal specifically targets the gross tumor volume (GTV) and the necrotic regions of the brain tumors on contrast enhanced T1-weighted MR images, requiring an initiali”