Shape-based pose estimation for automatic standard views of the knee
Lisa Kausch Sarina Thomas Holger Kunze Jan Siad El Barbari Klaus Maier-Hein
Surgical treatment of complicated knee fractures is guided by real-time
imaging using a mobile C-arm. Immediate and continuous control is achieved via
2D anatomy-specific standard views that correspond to a specific C-arm pose
relative to the patient positioning, which is currently determined manually,
following a trial-and-error approach at the cost of time and radiation dose.
The characteristics of the standard views of the knee suggests that the shape
information of individual bones could guide an automatic positioning procedure,
reducing time and the amount of unnecessary radiation during C-arm positioning.
To fully automate the C-arm positioning task during knee surgeries, we propose
a complete framework that enables (1) automatic laterality and standard view
classification and (2) automatic shape-based pose regression toward the desired
standard view based on a single initial X-ray. A suitable shape representation
is proposed to incorporate semantic information into the pose regression
pipeline. The pipeline is designed to handle two distinct standard views
simultaneously. Experiments were conducted to assess the performance of the
proposed system on 3528 synthetic and 1386 real X-rays for the a.-p. and
lateral standard. The view/laterality classificator resulted in an accuracy of
100\%/98\% on the simulated and 99\%/98\% on the real X-rays. The pose
regression performance was
$d\theta_{a.-p}=5.8\pm3.3\degree,\,d\theta_{lateral}=3.7\pm2.0\degree$ on the
simulated data and
$d\theta_{a.-p}=7.4\pm5.0\degree,\,d\theta_{lateral}=8.4\pm5.4\degree$ on the
real data outperforming intensity-based pose regression.