Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913948343730176 |
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| author | Thomas, Akhil John Boerkamp, Christiaan |
| author_facet | Thomas, Akhil John Boerkamp, Christiaan |
| contents | Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline models and highlight the importance of tailored parameter selection in automated nerve detection. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning Thomas, Akhil John Boerkamp, Christiaan Image and Video Processing Computer Vision and Pattern Recognition Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline models and highlight the importance of tailored parameter selection in automated nerve detection. |
| title | Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.13394 |