A Multi-atlas Approach for the Automatic Segmentation of Multiple Structures in Head and Neck CT Images
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3540
A multi-atlas approach is proposed for the automatic segmentation of nine different structures in a set of head and neck CT images for radiotherapy. The approach takes advantage of a training dataset of 25 images to build average head and neck atlases of high-quality. By registering patient images with the atlases at the global level, structures of interest are aligned approximately in space, which allowed multi-atlas-based segmentations and correlation-based label fusion to be performed at the local level in the following steps. Qualitative and quantitative evaluations are performed on a set of 15 testing images. As shown by the results, mandible, brainstem and parotid glands are segmented accurately (mean volume DSC>0.8). The segmentation accuracy for the optic nerves is also improved over previously reported results (mean DSC above 0.61 compared with 0.52 for previous results).

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Categories: Atlas-based segmentation, Deformable registration, Registration, Segmentation
Keywords: head and neck , radiotherapy, nonrigid registration, multi-atlas
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