Automated segmentation of the thyroid gland on CT using multi-atlas label fusion and random forest

摘要

The thyroid gland is an important endocrine organ. For a variety of clinical applications, a system for automated segmentation of the thyroid is desirable. Thyroid segmentation is challenging due to the inhomogeneous nature of the thyroid and the surrounding structures which have similar intensities. In this paper, we propose a fully automated method for thyroid detection and segmentation on CT scans. The thyroid gland is initially estimated by a multi-atlas segmentation with joint label fusion algorithm. The segmentation is then corrected by supervised statistical learning-based voxel labeling with a random forest algorithm. Multi-atlas label fusion transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest employs an ensemble of decision trees that are trained on labeled thyroids to recognize various features. The trained forest classifier is then applied to the estimated thyroid by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes and background non-thyroid voxels as negatives. We applied our method to 73 patients using 5 as atlases. The system achieved an overall 0.70 Dice Similarity Coefficient (DSC) if using the multi-atlas label fusion only and was improved to 0.75 DSC after the random forest correction.