Late fusion of deep learning and hand-crafted features for Achilles tendon healing monitoring
This work addresses the tedious and time-consuming diagnostic process for Achilles tendon healing monitoring, though it is incremental as it builds on an existing method.
The paper tackled the problem of automating Achilles tendon healing assessment by improving a previous deep learning method with hand-crafted features, resulting in significant correlation improvements across six parameters and up to 60% time savings in data acquisition.
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.