Carpal tunnel syndrome commonly occurs in individuals working in occupations that involve use of vibrating manual tools or tasks with highly repetitive and forceful manual exertion. In recent years, carpal tunnel syndrome has been evaluated by ultrasound imaging that monitors median nerve movement. Conventional image analysis methods, such as the active contour model, are typically used to expedite automatic segmentation of the median nerve, but these usually suffer from an arduous manual intervention. We propose a new convolutional neural network framework for localization and segmentation of the median nerve, called DeepNerve, that is based on the U-Net model. DeepNerve integrates the characteristics of MaskTrack and convolutional long short-term memory to effectively locate and segment the median nerve. On the basis of experimental results, the proposed model achieved high performance and generated average Dice measurement, precision, recall and F-score values of 0.8975, 0.8912, 0.9119 and 0.9015, respectively. The segmentation results of DeepNerve were significantly improved in comparison with those of conventional active contour models. Additionally, the results of Student's t-test revealed significant differences in four deformation measurements of the median nerve, including area, perimeter, aspect ratio and circularity. We conclude that the proposed DeepNerve not only generates satisfactory results for localization and segmentation of the median nerve, but also creates more promising measurements for applications in clinical carpal tunnel syndrome diagnosis.
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|Ultrasound in Medicine and Biology
|Published - 2020 9月
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