优化参数提升超声神经分割精度
Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning
- 基于DeepLabV3，自动调优阈值优化分割流程
- Dice达0.78，IoU达0.70，像素准确率0.95
- 适合医学图像分割与自动化诊断研究者
神经分割在医学影像中对精准识别神经结构至关重要。本研究提出一种基于DeepLabV3的优化分割流程，通过自动化阈值微调、预处理步骤改进及参数优化，显著提升分割性能。在超声神经影像上，实现Dice Score为0.78，IoU为0.70，像素准确率为0.95。结果表明，相较于基线模型有明显提升，凸显了定制化参数选择在自动化神经检测中的重要性。
原文摘要 · Abstract (English)
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.
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