评估在臂丛超声成像中基于深度学习的神经分割在现实数据约束下的性能
Evaluating Deep Learning-Based Nerve Segmentation in Brachial Plexus Ultrasound Under Realistic Data Constraints
AI总结：
本研究评估了基于深度学习的臂丛超声神经分割在不同数据集和标注策略下的性能，发现多源数据训练可提升鲁棒性，但多类监督导致神经分割精度下降，且小神经仍是主要挑战。
摘要：
准确的神经定位对超声引导区域麻醉的成功至关重要，然而由于图像对比度低、斑点噪声和患者间解剖差异，手动识别仍然具有挑战性。本研究使用U-Net架构评估臂丛超声图像中基于深度学习的神经分割，重点研究数据集组成和标注策略如何影响分割性能。我们发现，使用多种超声机器（SIEMENS ACUSON NX3 Elite和Philips EPIQ5）的联合数据训练可为表现较差的采集源提供正则化益处，尽管在匹配目标领域时仍无法超越单源训练。将任务从二分类神经分割扩展到多类监督（动脉、静脉、神经、肌肉）导致神经特定的Dice分数降低，性能下降幅度从9%到61%，这可能由于类别不平衡和边界模糊。此外，我们观察到神经大小与分割准确性之间存在中等正相关（Pearson r=0.587，p<0.001），表明较小的神经仍是主要挑战。这些发现为在现实临床数据约束下开发稳健的超声神经分割系统提供了方法学指导。
英文摘要：
Accurate nerve localization is critical for the success of ultrasound-guided regional anesthesia, yet manual identification remains challenging due to low image contrast, speckle noise, and inter-patient anatomical variability. This study evaluates deep learning-based nerve segmentation in ultrasound images of the brachial plexus using a U-Net architecture, with a focus on how dataset composition and annotation strategy influence segmentation performance. We find that training on combined data from multiple ultrasound machines (SIEMENS ACUSON NX3 Elite and Philips EPIQ5) provides regularization benefits for lower-performing acquisition sources, though it does not surpass single-source training when matched to the target domain. Extending the task from binary nerve segmentation to multi-class supervision (artery, vein, nerve, muscle) results in decreased nerve-specific Dice scores, with performance drops ranging from 9% to 61% depending on dataset, likely due to class imbalance and boundary ambiguity. Additionally, we observe a moderate positive correlation between nerve size and segmentation accuracy (Pearson r=0.587, p<0.001), indicating that smaller nerves remain a primary challenge. These findings provide methodological guidance for developing robust ultrasound nerve segmentation systems under realistic clinical data constraints.
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