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.

Fig. 1

Left: CT image with segmentation of left parotid (yellow: manual, red: patch-based). Right: Magnification of the blue patch (top) with manual segmentation (bottom). The four most similar patches in the repository vote for background (black at the center location), although the patch belongs to the left parotid. Intensity values of patches are normalized for visualization.

Fig. 2

Gaussian process segmentation of parotid gland. The initial label from the atlas-based segmentation only partially agrees with the manual segmentation. We extract contours from the image and use them in the kernel function k that allows us to sample label maps $L~\mathcal{G}\mathcal{P}(0,k)$, supported by the image. Conditioning these on the atlas labels results in an improved segmentation.

Fig. 3

Dice volume overlap and modified Hausdorff distance for left and right parotid glands. Red line indicates the median, the boxes extend to the 25^th and 75^th percentiles, and the whiskers reach to the most extreme values not considered outliers (red crosses). *, **, and *** indicate significance levels at 0.05, 0.01, and 0.001. For each baseline method (NLM, RF), the performance of the basic method, the variant that employs spectral label fusion (SLF) [14] and the variant based on Gaussian processes proposed here (GP) is reported.

Fig. 4

Examples of automatic segmentation results for different methods are shown in yellow. Manual delineations are shown in red.