Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

ABSTRACT:

Purpose

To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT).

Methods and materials

The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the

SUBMITTER: Yang X 

PROVIDER: S-EPMC4362545 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature