Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy.

Visual Information Processing Group, Department of Computing, Imperial College London, 180 Queen's Gate, London, SW7 2AZ, UK.
NeuroImage (Impact Factor: 6.13). 03/2009; 46(3):726-38. DOI: 10.1016/j.neuroimage.2009.02.018
Source: PubMed

ABSTRACT Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

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    ABSTRACT: Multi-atlas methods have been successful for brain segmentation, but their application to smaller anatomies remains relatively unexplored. We evaluate 7 statistical and voting-based label fusion algorithms (and 6 additional variants) to segment the optic nerves, eye globes and chiasm. For non-local STAPLE, we evaluate different intensity similarity measures (including mean square difference, locally normalized cross correlation, and a hybrid approach). Each algorithm is evaluated in terms of the Dice overlap and symmetric surface distance metrics. Finally, we evaluate refinement of label fusion results using a learning based correction method for consistent bias correction and Markov random field regularization. The multi-atlas labeling pipelines were evaluated on a cohort of 35 subjects including both healthy controls and patients. Across all three structures, NLSS with a mixed weighting type provided the most consistent results; for the optic nerve NLSS resulted in a median Dice similarity coefficient of 0.81, mean surface distance of 0.41 mm and Hausdorff distance 2.18 mm for the optic nerves. Joint label fusion resulted in slightly superior median performance for the optic nerves (0.82, 0.39 mm and 2.15 mm), but slightly worse on the globes. The fully automated multi-atlas labeling approach provides robust segmentations of orbital structures on MRI even in patients for whom significant atrophy (optic nerve head drusen) or inflammation (multiple sclerosis) is present.
    Journal of medical imaging (Bellingham, Wash.). 07/2014; 1(2).
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    ABSTRACT: Atlas based segmentation techniques have been proven to be effective in many automatic segmentation applications. However, the re-liance on image correspondence means that the segmentation results can be affected by any registration errors which occur, particularly if there is a high degree of anatomical variability. This paper presents a novel multi-resolution patch-based segmentation framework which is able to work on images without requiring registration. Additionally, an image similarity metric using 3D histograms of oriented gradients is proposed to enable atlas selection in this context. We applied the proposed approach to seg-ment MR images of the knee from the MICCAI SKI10 Grand Challenge, where 100 training atlases are provided and evaluation is conducted on 50 unseen test images. The proposed method achieved good scores over-all and is comparable to the top entries in the challenge for cartilage segmentation, demonstrating good performance when comparing against state-of-the-art approaches customised to Knee MRI.
    MICCAI workshop on Machine Learning in Medical Imaging; 01/2013
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    ABSTRACT: Background and purposeMulti-atlas segmentation can yield better results than single atlas segmentation, but practical applications are limited by long calculation times for deformable registration. To shorten the calculation time pre-calculated registrations of atlases could be linked via a single atlas registered in runtime to the current patient. The primary purpose of this work is to investigate and quantify segmentation quality changes introduced by such linked registrations. We also determine the optimal parameters for fusing linked multi-atlas labels using probabilistic weighted fusion.Material and methodsComputed tomography images of 10 head and neck cancer patients were used as atlases, with parotid glands, submandibular glands, the mandible and lymph node levels II-IV segmented by an experienced radiation oncologist following published consensus guidelines. The change in segmentation quality scored by Dice similarity coefficient (DSC) for linking free-form deformable registrations, modeled by B-splines, was investigated for both single- and multi-atlas label fusion by using a leave-one-out approach.ResultsThe median decrease of the DSC was in the range 2.8% to 8.4% compared to direct registrations for all structures while reducing the computer calculation time to that of a single deformable registration. Linking several registrations showed a DSC decrease almost linear to the number of links, suggesting that extrapolation to zero links provides an observer independent measure of the inherent precision with which the segmentation guidelines can be applied.Conclusions Linking pre-made registrations of multiple atlases via a runtime registration of a single atlas provides a feasible method for reducing computation time in multi-atlas registration.
    Radiation Oncology 12/2014; 9(1):251. · 2.36 Impact Factor

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