  Healing process assessment of the Achilles tendon is usually a complex
procedure that relies on a combination of biomechanical and medical imaging
tests. As a result, diagnostics remains a tedious and long-lasting task.
Recently, a novel method for the automatic assessment of tendon healing based
on Magnetic Resonance Imaging and deep learning was introduced. The method
assesses six parameters related to the treatment progress utilizing a modified
pre-trained network, PCA-reduced space, and linear regression. In this paper,
we propose to improve this approach by incorporating hand-crafted features. We
first perform a feature selection in order to obtain optimal sets of mixed
hand-crafted and deep learning predictors. With the use of approx. 20,000 MRI
slices, we then train a meta-regression algorithm that performs the tendon
healing assessment. Finally, we evaluate the method against scores given by an
experienced radiologist. In comparison with the previous baseline method, our
approach significantly improves correlation in all of the six parameters
assessed. Furthermore, our method uses only one MRI protocol and saves up to
60\% of the time needed for data acquisition.
