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Proceedings Paper

Propagating labels of the human brain based on non-rigid MR image registration: an evaluation
Author(s): Rolf A. Heckemann; Joseph V. Hajnal; Daniel Rueckert; Derek L. G. Hill; Alexander Hammers
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Paper Abstract

Background: In order to perform statistical analysis of cohorts based on images, reliable methods for automated anatomical segmentation are required. Label propagation (LP) from manually segmented atlases onto newly acquired images is a particularly promising approach. Methods: We investigated LP on a set of 6 three-dimensional T1-weighted magnetic resonance data sets of the brains of normal individuals. For each image, a manually prepared segmentation of 67 structures was available. Each subject image was used in turn as an atlas and registered non-rigidly to each other subject's image. The resulting transformations were applied to the label sets, yielding five different generated segmentations for each subject, which we compared with the native manual segmentations using an overlap measure (similarity index, SI). We then reviewed the LP results for five structures with varied anatomical and label characteristics visually to determine how the registration procedure had affected the delineation of their boundaries. Results: The majority of structures propagated well as measured by SI (SI > 70 in 80% of measurements). Boundaries that were marked in the atlas image by definite intensity differences were congruent, with good agreement between the manual and the generated segmentations. Some boundaries in the manual segmentation were defined as planes marked by landmarks; such boundaries showed greater mismatch. In some cases, the proximity of structures with similar intensity distorted the LP results: e.g., parts of the parahippocampal gyrus were labeled as hippocampus in two cases. Conclusion: The size and shape of anatomical structures can be determined reliably using label propagation, especially where boundaries are defined by distinct differences in grey scale image intensity. These results will inform further work to evaluate potential clinical uses of information extracted from images in this way.

Paper Details

Date Published: 29 April 2005
PDF: 8 pages
Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.597069
Show Author Affiliations
Rolf A. Heckemann, MRC Clinical Sciences Ctr., Imperial College at Hammersmith Hospital Campus (United Kingdom)
Joseph V. Hajnal, MRC Clinical Sciences Ctr., Imperial College at Hammersmith Hospital Campus (United Kingdom)
Daniel Rueckert, Imperial College London (United Kingdom)
Derek L. G. Hill, Univ. College London (United Kingdom)
Alexander Hammers, MRC Clinical Sciences Ctr., Imperial College at Hammersmith Hospital Campus (United Kingdom)


Published in SPIE Proceedings Vol. 5747:
Medical Imaging 2005: Image Processing
J. Michael Fitzpatrick; Joseph M. Reinhardt, Editor(s)

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