TY - GEN
T1 - Adaptive local multi-atlas segmentation: Application to heart segmentation in chest CT scans
AU - van Rikxoort, Eva M.
AU - Isgum, Ivana
AU - Staring, Marius
AU - Klein, Stefan
AU - van Ginneken, Bram
PY - 2008
Y1 - 2008
N2 - Atlas-based segmentation is a popular generic technique for automated delineation of structures in volumetric data sets. Several studies have shown that multi-atlas based segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on large volumetric data is too time-consuming for routine clinical use. We propose a generally applicable adaptive local multi-atlas segmentation method (ALMAS) that locally decides how many and which atlases are needed to segment a target image. Only the selected parts of atlases are registered. The method is iterative and automatically stops when no further improvement is expected. ALMAS was applied to segmentation of the heart on chest CT scans and compared to three existing atlas-based methods. It performed significantly better than single-atlas methods and as good as multi-atlas methods at a much lower computational cost.
AB - Atlas-based segmentation is a popular generic technique for automated delineation of structures in volumetric data sets. Several studies have shown that multi-atlas based segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on large volumetric data is too time-consuming for routine clinical use. We propose a generally applicable adaptive local multi-atlas segmentation method (ALMAS) that locally decides how many and which atlases are needed to segment a target image. Only the selected parts of atlases are registered. The method is iterative and automatically stops when no further improvement is expected. ALMAS was applied to segmentation of the heart on chest CT scans and compared to three existing atlas-based methods. It performed significantly better than single-atlas methods and as good as multi-atlas methods at a much lower computational cost.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=43449121567&origin=inward
U2 - https://doi.org/10.1117/12.772301
DO - https://doi.org/10.1117/12.772301
M3 - Conference contribution
VL - 6914
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2008: Image Processing
T2 - Medical Imaging 2008: Image Processing
Y2 - 17 February 2008 through 19 February 2008
ER -