Estimating Achilles Tendon Healing Progress with Convolutional Neural Networks

Abstract

Quantitative assessment of a treatment progress in the Achilles tendon healing process -one of the most common musculoskeletal disorders in modern medical practice -is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts for proper assessment. In this paper, we propose to significantly reduce the complexity of this process by using a novel method based on a pre-trained convolutional neural network. We first train our neural network on over 500 000 2D axial cross-sections from over 3 000 3D MRI studies to classify MRI images as belonging to a healthy or injured class, depending on the patients condition. We then take the outputs of a modified pre-trained network and apply linear regression on the PCAreduced space of the features to assess treatment progress. Our method allows to reduce up to 5-fold the amount of data needed to be registered during the MRI scan without any information loss. Furthermore, we are able to predict the healing process phase with equal accuracy to human experts in 3 out of 6 main criteria. Finally, contrary to the current approaches to healing assessment that rely on radiologist subjective opinion, our method allows to objectively compare different treatments methods which can lead to faster patients recovery.

Monitoring Achilles Tendon Healing Progress in Ultrasound Imaging with Convolutional Neural Networks

“…Although this method was proven to work for MRI scans [4], for ultrasound we observed a very weak correlation with actual healing parameters, which should be attributed to lower variance preserved by the first principal components and higher variance between scans from one examination. Therefore we do not present the results here.…”

“…On the other hand, sharpness of the tendon edges (STE), tendon edema (TE) and tissue edema (TisE) are typically evaluated on axial slices and for STE and TisE, all our networks achieve lower MAE and higher mean correlation when trained in the axial plane. In comparison with the results from [4], we notice that a convolutional neural network is able to achieve a better accuracy of binary classification on MRI data rather than US data (99.83% vs. 91.6% for the best respective models). Furthermore, a high correlation of automated method output with the ground truth in terms of three parameters: TE, TisE and STE has been reported for MRI scans.…”

“…In this study, we present a method for the automatic evaluation of the healing process of reconstructed Achilles tendon based on CNNs. We extend the approach proposed in [4] to US images in the axial and the sagittal plane and develop a novel method for healing phase estimation. To our knowledge, there are no other approaches in the literature to quantitatively asses the process of tendon healing through automated analyses of MRI and US imaging.…”

Multi-Step Segmentation Algorithm for Quantitative Magnetic Resonance Imaging T2 Mapping of Ruptured Achilles Tendons

“…The combination of the segmentation algorithm with feature analysis highlights the need to advance to the next stage of treatment and rehabilitation. Further studies are required, however, to analyse the healing process [39].…”

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

“…Advances in medical imaging technologies and methods allow CNNs to be used in orthodontics to shorten the planning time of orthodontic treatment, including an automatic search of landmarks on cephalometric X-ray images, tooth segmentation on Cone-Beam Computed Tomography (CBCT) images or digital models, and classification of defects on X-Ray panoramic images [2]. This type of network works very well for pattern analysis and recognition [3], object tracking [4], and medical image analysis [5], [6], [7]. In this work, we describe the current methods, the architectures of deep convolutional neural networks used in CBCT.…”