title: Contour-driven regression for label inference in atlas-based segmentation.
abstract: 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.
authors: Computer Science and Artificial Intelligence Lab, MIT, USA.
authors: Christian
authors: C
authors: Wachinger
authors: Massachusetts General Hospital, Harvard Medical School, USA.
authors: Gregory C
authors: GC
authors: Sharp
authors: Computer Science and Artificial Intelligence Lab, MIT, USA.
authors: Polina
authors: P
authors: Golland
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*