Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization. (July 2015) - Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro
Highlights: Automatic and accurate 3D active contour method for MRI liver segmentation. New approach to enhance the contrast in the input MRI image. The proposed methodology replaces the input image by a model based probability map. The minimization of this model is achieved by means of dual approach of Chambolle. Validation of the performance of the method with well-established quality metrics. Abstract: Liver cancer is one of the leading causes of cancer-related mortality worldwide. Non-invasive techniques of medical imaging such as Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) are often used by radiologists for diagnosis and surgery planning. With the aim of assuring the most reliable intervention planning to surgeons, new accurate methods and tools must be provided to locate and segment the regions of interest. Automated liver segmentation is a challenging problem for which promising results have been achieved mostly for CT. However, MRI is required by radiologists, since it offers better information for diagnosis purposes. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise, low contrast and poorly defined edges of the liver in relation to adjacent organs. In this paper, we present a method for MRI automatic 3D liver segmentation by means of an active contour model extended to 3D and minimized by total variation dual approach that has also been extended to 3D. A new approach to enhance the contrast in the input MRI image is proposed and it allows more accurate segmentation. The proposed methodology allows replacing the input image by a probability map obtained by means of a previously generated statistical model of the liver. An Accuracy of 98.89 and Dice Similarity Coefficient of 90.19 are in line with other state-of-the-art methodologies.