Multi-atlas segmentation with augmented features for cardiac MR images

Bai W, Shi W, Ledig C, Rueckert D (2015)

Publication Type: Journal article

Publication year: 2015

Journal

Medical Image Analysis Elsevier

Book Volume: 19

Pages Range: 98-109

Journal Issue: 1

DOI: 10.1016/j.media.2014.09.005

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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Bai, W., Shi, W., Ledig, C., & Rueckert, D. (2015). Multi-atlas segmentation with augmented features for cardiac MR images. Medical Image Analysis, 19(1), 98-109. https://doi.org/10.1016/j.media.2014.09.005

MLA:

Bai, Wenjia, et al. "Multi-atlas segmentation with augmented features for cardiac MR images." Medical Image Analysis 19.1 (2015): 98-109.

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