  X-ray image guidance enables percutaneous alternatives to complex procedures.
Unfortunately, the indirect view onto the anatomy in addition to projective
simplification substantially increase the task-load for the surgeon. Additional
3D information such as knowledge of anatomical landmarks can benefit surgical
decision making in complicated scenarios. Automatic detection of these
landmarks in transmission imaging is challenging since image-domain features
characteristic to a certain landmark change substantially depending on the
viewing direction. Consequently and to the best of our knowledge, the above
problem has not yet been addressed. In this work, we present a method to
automatically detect anatomical landmarks in X-ray images independent of the
viewing direction. To this end, a sequential prediction framework based on
convolutional layers is trained on synthetically generated data of the pelvic
anatomy to predict 23 landmarks in single X-ray images. View independence is
contingent on training conditions and, here, is achieved on a spherical segment
covering (120 x 90) degrees in LAO/RAO and CRAN/CAUD, respectively, centered
around AP. On synthetic data, the proposed approach achieves a mean prediction
error of 5.6 +- 4.5 mm. We demonstrate that the proposed network is immediately
applicable to clinically acquired data of the pelvis. In particular, we show
that our intra-operative landmark detection together with pre-operative CT
enables X-ray pose estimation which, ultimately, benefits initialization of
image-based 2D/3D registration.
