Ronan Flanagan

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In this paper, a novel segmentation method for liver vasculature is presented, intended for numerical simulation of radio frequency ablation (RFA). The developed method is a semiautomatic hybrid based on multi-scale vessel enhancement combined with ridge-oriented region growing and skeleton-based postprocessing. In addition, an interactive tool for(More)
Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition(More)
—The web-based Go-Smart environment is a scalable system that allows the prediction of minimally invasive cancer treatment. Interventional radiologists create a patient-specific 3D model by semi-automatic segmentation and registration of pre-interventional CT (Computed Tomography) and/or MRI (Magnetic Resonance Imaging) images in a 2D/3D browser(More)
The preprocessing of the input data varies depending on the target application area. As the main focus of this paper lies in the medical domain , we will describe the preprocessing step for the medical data used for planning the RFA of liver tumors and brain tumor resection. Note that the operation of our path safety computation system is fully de-coupled(More)
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