Difference between revisions of "Projects:NonparametricSegmentation"
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We propose a non-parametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms we develop rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute a final segmentation of the test subject. Label fusion methods have been shown to yield accurate segmentation, since the use | We propose a non-parametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms we develop rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute a final segmentation of the test subject. Label fusion methods have been shown to yield accurate segmentation, since the use | ||
of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures, cf. [1,2,3]. To the best of our knowledge, this project presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multi-atlas segmentation algorithms are interpreted as special cases of our framework. | of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures, cf. [1,2,3]. To the best of our knowledge, this project presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multi-atlas segmentation algorithms are interpreted as special cases of our framework. | ||
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| + | We conduct two sets of experiments to validate our framework. In the first set of experiments, we use 39 brain MRI scans – with manually segmented white matter, cerebral cortex, ventricles and subcortical structures – to compare different label fusion algorithms and the widely-used Freesurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than Freesurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 304 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal atrophy that foreshadows the onset of Alzheimer’s Disease. | ||
Revision as of 18:57, 10 September 2009
Home < Projects:NonparametricSegmentationIntroduction
We propose a non-parametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms we develop rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute a final segmentation of the test subject. Label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures, cf. [1,2,3]. To the best of our knowledge, this project presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multi-atlas segmentation algorithms are interpreted as special cases of our framework.
We conduct two sets of experiments to validate our framework. In the first set of experiments, we use 39 brain MRI scans – with manually segmented white matter, cerebral cortex, ventricles and subcortical structures – to compare different label fusion algorithms and the widely-used Freesurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than Freesurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 304 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal atrophy that foreshadows the onset of Alzheimer’s Disease.
Literature
[1] X. Artaechevarria, A. Munoz-Barrutia, and C. Ortiz de Solorzano. Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Tran. Med. Imaging, 28(8):1266 – 1277, 2009.
[2] R.A. Heckemann, J.V. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage, 33(1):115–126, 2006.
[3] T. Rohlfing, R. Brandt, R. Menzel, and C.R. Maurer. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage, 21(4):1428–1442, 2004.
