Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more accurately by using Monte Carlo sampling. We demonstrate our technique by sampling atlas warps in a recent method for hippocampal subfield segmentation, and show a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the method also yields informative “error bars” on the segmentation results for each of the individual sub-structures.
|Title of host publication||Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 : 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III|
|Publication status||Published - 2012|
|Event||15th International Conference on Medical Image Computing and Computer Assisted Intervention - Nice, France|
Duration: 1 Oct 2012 → 5 Oct 2012
|Conference||15th International Conference on Medical Image Computing and Computer Assisted Intervention|
|Period||01/10/2012 → 05/10/2012|
|Series||Lecture Notes in Computer Science|