Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

Chen, Cheng; Xie, W.; Franke, J.; Grutzner, P.A.; Nolte, Lutz-Peter; Zheng, Guoyan (2014). Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements. Medical image analysis, 18(3), pp. 487-499. Elsevier 10.1016/j.media.2014.01.002

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In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements
from all patches together in a data driven way, by considering not only the training data
but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

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