Fully automatic X-ray image segmentation via joint estimation of image displacements.

Chen, Cheng; Xie, Weiguo; Franke, Jochen; Grützner, Paul A.; Nolte, Lutz-Peter; Zheng, Guoyan (2013). Fully automatic X-ray image segmentation via joint estimation of image displacements. In: Kensaku, Mori; Ichiro, Sakuma; Yoshinobu, Sato; Christian, Barillot; Nassir, Navab (eds.) 16 th International Conference of Medical image computing and computer-assisted intervention (MICCAI 2013), Proceedings. Lecture Notes in Computer Science: Vol. 16 (pp. 227-234). Springer 10.1007/978-3-642-40760-4_29

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We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

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