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 and , respectively. The system with sequential multiatlas label fusion followed by RF correction increased the dice similarity coefficient to and improved the segmentation accuracy.