Multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images

We present a framework for multi-atlas based segmentation in situations where we have a small number of segmented atlas images, but a large database of unlabeled images is also available. The novelty lies in the application of graph-based registration on a manifold to the problem of multi-atlas registration. The approach is to place all the images in a learned manifold space and construct a graph connecting near neighbors. Atlases are selected for any new image to be segmented based on the shortest path length along the manifold graph. A multi-scale non-rigid registration takes place via each of the nodes on the graph. The expectation is that by registering via similar images, the likelihood of misregistrations is reduced. Having registered multiple atlases via the graph, patch-based voxel weighted voting takes place to provide the final segmentation. We apply this approach to a set of T2 MRI images of the prostate, which is a notoriously difficult segmentation task. On a set of 25 atlas images and 85 images overall, we see that registration via the manifold graph improves the Dice coefficient from 0:82±0:05 to 0:86±0:03 and the average symmetrical boundary distance from 2:89±0:62mm to 2:47±0:51mm. This is a modest but potentially useful improvement in a difficult set of images. It is expected that our approach will provide similar improvement to any multi-atlas segmentation task where a large number of unsegmented images are available.