  Dynamic radiographic measurement of 3D TKA kinematics has provided important
information for implant design and surgical technique for over 30 years.
However, current methods of measuring TKA kinematics are too cumbersome or
time-consuming for practical clinical application. Even state-of-the-art
techniques require human-supervised initialization or human supervision
throughout the entire optimization process. Elimination of human supervision
could potentially bring this technology into clinical practicality. Therefore,
we propose a fully autonomous pipeline for quantifying TKA kinematics from
single-plane imaging. First, a convolutional neural network segments the
femoral and tibial implants from the image. Second, segmented images are
compared to Normalized Fourier Descriptor shape libraries for initial pose
estimates. Lastly, a Lipschitzian optimization routine minimizes the difference
between the segmented image and the projected implant. This technique reliably
reproduces human-supervised kinematics measurements from internal datasets and
external validation studies, with RMS differences of less than 0.7mm and
4{\deg} for internal studies and 0.8mm and 1.7{\deg} for external validation
studies. This performance indicates that it will soon be practical to perform
these measurements in a clinical setting.
