  This paper addresses the challenge of localization of anatomical landmarks in
knee X-ray images at different stages of osteoarthritis (OA). Landmark
localization can be viewed as regression problem, where the landmark position
is directly predicted by using the region of interest or even full-size images
leading to large memory footprint, especially in case of high resolution
medical images. In this work, we propose an efficient deep neural networks
framework with an hourglass architecture utilizing a soft-argmax layer to
directly predict normalized coordinates of the landmark points. We provide an
extensive evaluation of different regularization techniques and various loss
functions to understand their influence on the localization performance.
Furthermore, we introduce the concept of transfer learning from low-budget
annotations, and experimentally demonstrate that such approach is improving the
accuracy of landmark localization. Compared to the prior methods, we validate
our model on two datasets that are independent from the train data and assess
the performance of the method for different stages of OA severity. The proposed
approach demonstrates better generalization performance compared to the current
state-of-the-art.
