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Proceedings Paper

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

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.

Paper Details

Date Published: 27 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90350A (27 March 2014); doi: 10.1117/12.2044027
Show Author Affiliations
Qinquan Gao, Imperial College London (United Kingdom)
Tong Tong, Imperial College London (United Kingdom)
Daniel Rueckert, Imperial College London (United Kingdom)
PJ "Eddie" Edwards, Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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