Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images
Ultrasound imaging has been used to investigate compression of the median nerve in carpal tunnel syndrome patients. Ultrasound imaging and the extraction of median nerve parameters from ultrasound images are crucial and are usually performed manually by experts. The manual annotation of ultrasound i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/1/13 |
| _version_ | 1797343322201653248 |
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| author | Shion Ando Ping Yeap Loh |
| author_facet | Shion Ando Ping Yeap Loh |
| author_sort | Shion Ando |
| collection | DOAJ |
| description | Ultrasound imaging has been used to investigate compression of the median nerve in carpal tunnel syndrome patients. Ultrasound imaging and the extraction of median nerve parameters from ultrasound images are crucial and are usually performed manually by experts. The manual annotation of ultrasound images relies on experience, and intra- and interrater reliability may vary among studies. In this study, two types of convolutional neural networks (CNNs), U-Net and SegNet, were used to extract the median nerve morphology. To the best of our knowledge, the application of these methods to ultrasound imaging of the median nerve has not yet been investigated. Spearman’s correlation and Bland–Altman analyses were performed to investigate the correlation and agreement between manual annotation and CNN estimation, namely, the cross-sectional area, circumference, and diameter of the median nerve. The results showed that the intersection over union (IoU) of U-Net (0.717) was greater than that of SegNet (0.625). A few images in SegNet had an IoU below 0.6, decreasing the average IoU. In both models, the IoU decreased when the median nerve was elongated longitudinally with a blurred outline. The Bland–Altman analysis revealed that, in general, both the U-Net- and SegNet-estimated measurements showed 95% limits of agreement with manual annotation. These results show that these CNN models are promising tools for median nerve ultrasound imaging analysis. |
| first_indexed | 2024-03-08T10:45:56Z |
| format | Article |
| id | doaj.art-103b6072b9af4b61899c2294a5b14136 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-08T10:45:56Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-103b6072b9af4b61899c2294a5b141362024-01-26T17:11:01ZengMDPI AGJournal of Imaging2313-433X2024-01-011011310.3390/jimaging10010013Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound ImagesShion Ando0Ping Yeap Loh1Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, Fukuoka 819-0395, JapanDepartment of Human Life Design and Science, Faculty of Design, Kyushu University, Fukuoka 819-0395, JapanUltrasound imaging has been used to investigate compression of the median nerve in carpal tunnel syndrome patients. Ultrasound imaging and the extraction of median nerve parameters from ultrasound images are crucial and are usually performed manually by experts. The manual annotation of ultrasound images relies on experience, and intra- and interrater reliability may vary among studies. In this study, two types of convolutional neural networks (CNNs), U-Net and SegNet, were used to extract the median nerve morphology. To the best of our knowledge, the application of these methods to ultrasound imaging of the median nerve has not yet been investigated. Spearman’s correlation and Bland–Altman analyses were performed to investigate the correlation and agreement between manual annotation and CNN estimation, namely, the cross-sectional area, circumference, and diameter of the median nerve. The results showed that the intersection over union (IoU) of U-Net (0.717) was greater than that of SegNet (0.625). A few images in SegNet had an IoU below 0.6, decreasing the average IoU. In both models, the IoU decreased when the median nerve was elongated longitudinally with a blurred outline. The Bland–Altman analysis revealed that, in general, both the U-Net- and SegNet-estimated measurements showed 95% limits of agreement with manual annotation. These results show that these CNN models are promising tools for median nerve ultrasound imaging analysis.https://www.mdpi.com/2313-433X/10/1/13carpal tunnel syndromeU-NetSegNetsemantic segmentation |
| spellingShingle | Shion Ando Ping Yeap Loh Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images Journal of Imaging carpal tunnel syndrome U-Net SegNet semantic segmentation |
| title | Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images |
| title_full | Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images |
| title_fullStr | Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images |
| title_full_unstemmed | Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images |
| title_short | Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images |
| title_sort | convolutional neural network approaches in median nerve morphological assessment from ultrasound images |
| topic | carpal tunnel syndrome U-Net SegNet semantic segmentation |
| url | https://www.mdpi.com/2313-433X/10/1/13 |
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