title: Multi-atlas segmentation with augmented features for cardiac MR images.
abstract: Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupé et al., 2011; Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features.
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom. Electronic address: w.bai@imperial.ac.uk.
authors.forename: Wenjia
authors.initials: W
authors.lastname: Bai
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom.
authors.forename: Wenzhe
authors.initials: W
authors.lastname: Shi
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom.
authors.forename: Christian
authors.initials: C
authors.lastname: Ledig
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D018487:Ventricular Dysfunction, Left / Q000473:pathology*
keywords: Augmented features; Cardiac image segmentation; Multi-atlas segmentation; Patch-based segmentation