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Journal of Medical Imaging

Automated segmentation of the thyroid gland on thoracic CT scans by multiatlas label fusion and random forest classification
Author(s): Divya Narayanan; Jiamin Liu; Lauren M. Kim; Kevin W. Chang; Le Lu; Jianhua Yao; Evrim B. Turkbey; Ronald M. Summers
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Paper Abstract

The thyroid is an endocrine gland that regulates metabolism. Thyroid image analysis plays an important role in both diagnostic radiology and radiation oncology treatment planning. Low tissue contrast of the thyroid relative to surrounding anatomic structures makes manual segmentation of this organ challenging. This work proposes a fully automated system for thyroid segmentation on CT imaging. Following initial thyroid segmentation with multiatlas joint label fusion, a random forest (RF) algorithm was applied. Multiatlas label fusion transfers labels from labeled atlases and warps them to target images using deformable registration. A consensus atlas solution was formed based on optimal weighting of atlases and similarity to a given target image. Following the initial segmentation, a trained RF classifier employed voxel scanning to assign class-conditional probabilities to the voxels in the target image. Thyroid voxels were categorized with positive labels and nonthyroid voxels were categorized with negative labels. Our method was evaluated on CT scans from 66 patients, 6 of which served as atlases for multiatlas label fusion. The system with independent multiatlas label fusion method and RF classifier achieved average dice similarity coefficients of 0.72±0.13 and 0.57±0.14, respectively. The system with sequential multiatlas label fusion followed by RF correction increased the dice similarity coefficient to 0.76±0.11 and improved the segmentation accuracy.

Paper Details

Date Published: 30 December 2015
PDF: 8 pages
J. Med. Imag. 2(4) 044006 doi: 10.1117/1.JMI.2.4.044006
Published in: Journal of Medical Imaging Volume 2, Issue 4
Show Author Affiliations
Divya Narayanan, National Institutes of Health (United States)
Jiamin Liu, National Institutes of Health (United States)
Lauren M. Kim, National Institutes of Health (United States)
Kevin W. Chang, National Institutes of Health (United States)
Le Lu, National Institutes of Health (United States)
Jianhua Yao, National Institutes of Health (United States)
Evrim B. Turkbey, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)

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