Abstract
In this last decade, multiple-atlas segmentation (MAS) has emerged as a promising technique for medical image segmentation. In MAS, a novel target image is segmented by fusing the label maps of a set of annotated images (or atlases), after spatial normalization. Weighted voting is a well-known label fusion strategy consisting of computing each target label as a weighted average of the atlas labels in a local neighborhood. The weights, denoting the local anatomical similarity of the candidate atlases, are often approximated using image-patch similarity measurements. Such an approach, known as patch-based label fusion (PBLF), may fail to discriminate the anatomically relevant patches in challenging regions with high label variability. In order to overcome this limitation we propose a supervised method that embeds the original image patches onto a space that emphasizes the appearance characteristics that are critical for a correct labeling, while supressing the irrelevant ones. We show that PBLF using the embedded patches compares favourably with state-of-the-art methods in brain MR image segmentation experiments.
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Notes
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The estimated label map in the common space \(\hat{F}\) is finally transformed back to the original target space and thresholded to obtain the binary labels.
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Sanroma, G. et al. (2015). Discriminative Dimensionality Reduction for Patch-Based Label Fusion. In: Bhatia, K., Lombaert, H. (eds) Machine Learning Meets Medical Imaging. MLMMI 2015. Lecture Notes in Computer Science(), vol 9487. Springer, Cham. https://doi.org/10.1007/978-3-319-27929-9_10
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