Dataset Information

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 kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours.

Results

Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months.

Conclusions

We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy.

SUBMITTER: Yang X

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

REPOSITORIES: biostudies-literature

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Publications

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

Yang Xiaofeng X Wu Ning N Cheng Guanghui G Zhou Zhengyang Z Yu David S DS Beitler Jonathan J JJ Curran Walter J WJ Liu Tian T

International journal of radiation oncology, biology, physics 20141013 5

<h4>Purpose</h4>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).<h4>Methods and materials</h4>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 segmentat ...[more]

PMID: 25442347

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Project description:PurposeTo validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches-the commercial MIM, the open-source Elastix software, and an optimized version of it.Materials and methodsTwelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.ResultsA good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.ConclusionThe 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

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Dataset Information

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

Purpose

Methods and materials

Results

Conclusions

Publications

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

Similar Datasets

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