Abstract
In this paper, we present a multi-atlas segmentation method based on the level set formulation for performing label fusion that takes into account the image information and regularity of the region of interest (ROI). In the presented method, multiple atlases are first registered to a target image by deformable registration via attribute matching and mutual saliency weighting (DRAMMS) and advanced neuroimaging tools (ANTs) to get the warped labels. Then, an optimal labeling is sought by label fusion for segmentation of target image. Label fusion is achieved by seeking an optimal level set function which minimizes an energy functional in regards to three terms: label fusion term, image based term, and regularization term. In this work, we discussed the impact of subset on the accuracy of segment results. Results show that segmentation results will be much more accurate if an appropriate subset of atlases are selected for each target image than those given by non-selective combination of random atlas subsets.
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Acknowledgment
This research was partly supported by National Natural Science Foundation of China (NSFC) under Grant No. 61302012 and No. 61172002, the Fundamental Research Funds for the Central Universities under Grant N130418002, N120518001, N140403006 and N140402003. The National Key Technology support Program 2014BAI17B02, and Liaoning Natural Science Foundation under Grant No. 2013020021.
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Song, Y., Gong, Z., Zhao, D., Feng, C., Li, C. (2015). Impact of the Number of Atlases in a Level Set Formulation of Multi-atlas Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_48
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DOI: https://doi.org/10.1007/978-3-319-27857-5_48
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