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A Multi-atlas Approach for the Automatic Segmentation of Multiple Structures in Head and Neck CT Images

Chen, Antong, Dawant, Benoit
Merck & Co., Inc.
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Published in The MIDAS Journal - MICCAI 2015 Workshop: Head and Neck Auto Segmentation Challenge.
Submitted by Antong Chen on 2016-02-02 16:09:31.

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).