3D active surfaces for liver segmentation in multisequence MRI images.
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.
Tecnalia Research & Innovation, Computer Vision Area, Parque Tecnológico de Bizkaia, Derio 48160, Spain. Electronic address: aranzazu.bereciartua@tecnalia.com.
Arantza
A
Bereciartua
Tecnalia Research & Innovation, Computer Vision Area, Parque Tecnológico de Bizkaia, Derio 48160, Spain.
Artzai
A
Picon
Tecnalia Research & Innovation, Computer Vision Area, Parque Tecnológico de Bizkaia, Derio 48160, Spain.
Adrian
A
Galdran
Department of System Engineering and Automatic, University of the Basque Country, Bilbao, Spain.
Pedro
P
Iriondo
D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013499:Surface Properties
Active surface; Liver segmentation; Magnetic resonance imaging; Multichannel; Multivariate image descriptors; Variational techniques