Label fusion using performance estimation with iterative label selection

Authors:
T. R. Langerak;U. A. Van Der Heide;I. M. Lips;A. N. T. J. Kotte;M. Van Vulpen;J. P. W. Pluim
Affiliations:
Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands;Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands;Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands;Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands;Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands;Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

Atlas-based segmentation is a well-known method of automatically computing a segmentation. When multiple atlases are available, then each atlas can be used to compute a 'label', which is an estimation of the ground truth segmentation of a target image. By combining these labels, a more accurate approximation of the ground truth segmentation can be made. A common method to combine labels is the STAPLE algorithm, but this method fails when the performance of the labels highly varies. Other methods select labels based on their estimated performance, but combine them using a simple majority vote procedure. In this paper, a simpler variant of the STAPLE algorithm is presented that iteratively selects labels. Results are given that show that the proposed method outperforms STAPLE in an application to segmentation of the prostate.