  Achilles tendon rupture is a debilitating injury, which is typically treated
with surgical repair and long-term rehabilitation. The recovery, however, is
protracted and often incomplete. Diagnosis, as well as healing progress
assessment, are largely based on ultrasound and magnetic resonance imaging. In
this paper, we propose an automatic method based on deep learning for analysis
of Achilles tendon condition and estimation of its healing progress on
ultrasound images. We develop custom convolutional neural networks for
classification and regression on healing score and feature extraction. Our
models are trained and validated on an acquired dataset of over 250.000
sagittal and over 450.000 axial ultrasound slices. The obtained estimates show
a high correlation with the assessment of expert radiologists, with respect to
all key parameters describing healing progress. We also observe that parameters
associated with i.a. intratendinous healing processes are better modeled with
sagittal slices. We prove that ultrasound imaging is quantitatively useful for
clinical assessment of Achilles tendon healing process and should be viewed as
complementary to magnetic resonance imaging.
