Multi-atlas based methods have been a trend for robust and automated image segmentation. In general these methods first
transfer prior manual segmentations, i.e., label maps, on a set of atlases to a given target image through image registration.
These multiple label maps are then fused together to produce segmentations of the target image through voting strategy or
statistical fusing, e.g., STAPLE. STAPLE simultaneously estimates the true segmentation and the label map performance
level, but has been shown inaccurate for multi-atlas segmentation because it is determined completely on the propagated
label maps without considering the target image intensity. We develop a new method, called iSTAPLE, that combines
target image intensity into a similar maximum likelihood estimate (MLE) framework as in STAPLE to take advantage of
both intensity-based segmentation and statistical label fusion based on atlas consensus and performance level. The MLE
framework is then solved using a modified EM algorithm to simultaneously estimate the intensity profiles of structures of
interest as well as the true segmentation and atlas performance level. Unlike other methods, iSTAPLE does not require the
target image to have same image contrast and intensity range as the atlas images, which greatly extends the use of atlases.
Experiments on whole brain segmentation showed that iSTAPLE performed consistently better than STAPLE.