3D active surfaces for liver segmentation in multisequence MRI images. Issue 132 (August 2016) - Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro
Highlights: Fast automatic and accurate method for multichannel MRI 3D liver segmentation. The method proposes a model based 3D active surface for multichannel MRI. New approach to handle multichannel information by means of a compact descriptor. The compact descriptor replaces the input images by a model based probability map. Validation of the performance of the method with well established quality metrics. Abstract: Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.