Late fusion of deep learning and hand-crafted features for Achilles tendon healing monitoring

TL;DR

This paper enhances Achilles tendon healing assessment by combining deep learning and hand-crafted features, significantly improving accuracy and reducing MRI data acquisition time.

Contribution

It introduces a novel fusion of deep learning and hand-crafted features with feature selection and meta-regression for improved tendon healing evaluation.

Findings

Significantly improved correlation with radiologist scores

Uses only one MRI protocol

Reduces data acquisition time by up to 60%

Abstract

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.