Estimating Achilles Tendon Healing Progress with Convolutional Neural Networks
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 patient’s condition. We then take the outputs of a modified pre-trained network and apply linear regression on the PCA-reduced 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 patient’s recovery.
KeywordsAchilles tendon trauma Deep learning MRI
The following work was part of Novel Scaffold-based Tissue Engineering Approaches to Healing and Regeneration of Tendons and Ligaments (START) project, co-funded by The National Centre for Research and Development (Poland) within STRATEGMED programme (STRATEGMED1/233224/10/NCBR/2014).
- 1.Raikin, S.M.: Epidemiology of Achilles tendon rupture in the US. Lower Extremity Review (2014)Google Scholar
- 2.Kapinski, N., Zielinski, J., Borucki, B., Nowinski, K.: MRI-based deep learning for in-situ monitoring of achilles tendon regeneration process. Int. J. Comput. Assist. Radiol. Surg. 12(Supplement 1), 57–58 (2017)Google Scholar
- 3.Nowosielski, J., Zielinski, J., Borucki, B., Nowinski, K.: Multidimensional haralick’s feature space analysis for assessment of the achilles tendon in mr imaging. Int. J. Comput. Assist. Radiol. Surg. 12(Supplement 1), 218–220 (2017)Google Scholar
- 4.Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: CVPR (2014)Google Scholar
- 6.Bar, Y., Diamant, I., Wolf, L., Greenspan, H.: Deep learning with non-medical training used for chest pathology identification. In: SPIE Medical Imaging (2015)Google Scholar
- 7.Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
- 8.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR, vol. abs/1512.03385 (2015)Google Scholar
- 9.Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)Google Scholar