Counter-Driven Regression for Label Inference in Atlas-Based Segmentation
Author(s)Wachinger, Christian; Sharp, Gregory C.; Golland, Polina
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We present a novel method for inferring tissue labels in atlas-based image segmentation using Gaussian process regression. Atlas-based segmentation results in probabilistic label maps that serve as input to our method. We introduce a contour-driven prior distribution over label maps to incorporate image features of the input scan into the label inference problem. The mean function of the Gaussian process posterior distribution yields the MAP estimate of the label map and is used in the subsequent voting. We demonstrate improved segmentation accuracy when our approach is combined with two different patch-based segmentation techniques. We focus on the segmentation of parotid glands in CT scans of patients with head and neck cancer, which is important for radiation therapy planning.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Wachinger, Christian, Gregory C. Sharp, and Polina Golland. “Contour-Driven Regression for Label Inference in Atlas-Based Segmentation.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. Ed. Kensaku Mori et al. Vol. 8151. Springer Berlin Heidelberg, 2013. 211–218. Lecture Notes in Computer Science.
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