  X-ray based measurement and guidance are commonly used tools in orthopaedic
surgery to facilitate a minimally invasive workflow. Typically, a surgical
planning is first performed using knowledge of bone morphology and anatomical
landmarks. Information about bone location then serves as a prior for
registration during overlay of the planning on intra-operative X-ray images.
Performing these steps manually however is prone to intra-rater/inter-rater
variability and increases task complexity for the surgeon. To remedy these
issues, we propose an automatic framework for planning and subsequent overlay.
We evaluate it on the example of femoral drill site planning for medial
patellofemoral ligament reconstruction surgery. A deep multi-task stacked
hourglass network is trained on 149 conventional lateral X-ray images to
jointly localize two femoral landmarks, to predict a region of interest for the
posterior femoral cortex tangent line, and to perform semantic segmentation of
the femur, patella, tibia, and fibula with adaptive task complexity weighting.
On 38 clinical test images the framework achieves a median localization error
of 1.50 mm for the femoral drill site and mean IOU scores of 0.99, 0.97, 0.98,
and 0.96 for the femur, patella, tibia, and fibula respectively. The
demonstrated approach consistently performs surgical planning at expert-level
precision without the need for manual correction.
