Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2011.11777v1
• Date: Mon, 23 Nov 2020 22:41:41 GMT
• Title: Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks
• Authors: Mostafa Jahanifar, Neda Zamani Tajeddin, Meisam Hasani, Babak Shekarchi, Kamran Azema
• Abstract summary: An automatic tendinopathy recognition framework based on convolutional neural networks has been proposed. Tendon segmentation is done through a novel network, NASUNet. A general classification pipeline has been proposed for tendinopathy recognition.
• Score: 1.021325814813899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tendon injuries like tendinopathies, full and partial thickness tears are prevalent, and the supraspinatus tendon (SST) is the most vulnerable ones in the rotator cuff. Early diagnosis of SST tendinopathies is of high importance and hard to achieve using ultrasound imaging. In this paper, an automatic tendinopathy recognition framework based on convolutional neural networks has been proposed to assist the diagnosis. This framework has two essential parts of tendon segmentation and classification. Tendon segmentation is done through a novel network, NASUNet, which follows an encoder-decoder architecture paradigm and utilizes a multi-scale Enlarging cell. Moreover, a general classification pipeline has been proposed for tendinopathy recognition, which supports different base models as the feature extractor engine. Two feature maps comprising positional information of the tendon region have been introduced as the network input to make the classification network spatial-aware. To evaluate the tendinopathy recognition system, a data set consisting of 100 SST ultrasound images have been acquired, in which tendinopathy cases are double-verified by magnetic resonance imaging. In both segmentation and classification tasks, lack of training data has been compensated by incorporating knowledge transferring, transfer learning, and data augmentation techniques. In cross-validation experiments, the proposed tendinopathy recognition model achieves 91% accuracy, 86.67% sensitivity, and 92.86% specificity, showing state-of-the-art performance against other models.

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