Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers.

Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs.

Ultrasound Image Quality Evaluation using a Structural Similarity Based Autoencoder.

Ultrasound (US) imaging is a widely used clinical technique that requires extensive training to use correctly. Good quality US images are essential for effective interpretation of the results, however numerous sources of error can impair quality. Currently, image quality assessment is performed by an experienced sonographer through visual inspection, however this is usually unachievable by inexperienced users. An autoencoder (AE) is a machine learning technique that has been shown to be effective at anomaly detection and could be used for fast and effective image quality assessment. In this study, we explored the use of an AE to distinguish between good and poor-quality US images (caused by artifacts and noise) by using the reconstruction error to train and test a random forest classifier (RFC) for classification. Good and poor-quality ultrasound images were obtained from forty-nine healthy subjects and were used to train an AE using two different loss functions, with one based on the structural similarity index measure (SSIM) and the other on the mean squared error (MSE). The resulting reconstruction errors of each image were then used to classify the images into two groups based on quality by training and testing an RFC. Using the SSIM based AE, the classifier showed an average accuracy of 71%±4.0% when classifying images based on user errors and an accuracy of 91%±1.0% when sorting images based on noise. The respective accuracies obtained from the AE using the MSE function were 76%±2.0% and 83%±2.0%. The results of this study demonstrate that an AE has the potential to differentiate good quality US images from those with poor quality, which could be used to help less experienced researchers and clinicians obtain a more objective measure of image quality when using US.