Evaluation of Atlas Fusion Strategies for Segmentation of Head and Neck Lymph Nodes for Radiotherapy Planning

Accurate segmentation of lymph nodes in head and neck (H&N) CT images is essential for the radiotherapy planning of the H&N cancer. Atlas-based segmentation methods are widely used for the automated segmentation of such structures. Multi-atlas approaches are proven to be more accurate and robust than using a single atlas. We have recently proposed a general Markov random field (MRF)-based framework that can perform edge-preserving smoothing of the labels at the time of fusing the labels itself. There are three main contributions of this paper: First, we reformulate the "shape based averaging" (SBA) fusion method to fit into the general MRF-based fusion framework. Second, we evaluate the following fusion algorithms for the segmentation of H&N lymph nodes: (i) STAPLE, (ii) SBA, (iii) SBA+MRF, (iv) majority voting (MV), (v) MV+MRF, (vi) global weighted voting (GWV), (vii) GWV+MRF, (viii) local weighted voting (LWV) and (ix) LWV+MRF. Finally, we also study the effect varying the number of atlases on the performance of the above algorithms.


Published in:
Proceedings of the IEEE International Symposium on Biomedical Imaging
Presented at:
IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, May 2-5, 2012
Year:
2012
Publisher:
Barcelona, Ieee
ISBN:
978-1-4577-1858-8
Keywords:
Laboratories:




 Record created 2012-01-17, last modified 2018-01-28

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