用分步精修方法实现多电极在体实时精准定位，提升神经实验自动化水平。
Coarse-to-Fine Learning for Multi-Pipette Localisation in Robot-Assisted In Vivo Patch-Clamp
- 先粗后细分两阶段定位，结合GAN去噪增强电极可见性
- 在体多电极定位精度超98%（误差<10μm），平均误差仅2.52μm
- 适合需要高精度自动化电极定位的神经科学研究者
在体图像引导的多电极膜片钳技术对研究神经细胞相互作用与网络动态至关重要。然而，当前方法主要依赖人工操作，限制了可及性与扩展性。机器人自动化虽具前景，但实现实时多电极精准定位仍面临挑战。现有方法多聚焦于离体实验或单电极应用，难以适配在体多电极场景。为此，我们提出一种基于热图增强的分阶段学习方法，用于机器人辅助在体多电极定位。具体而言，引入生成对抗网络（GAN）模块去除背景噪声并提升电极可见性；随后采用两阶段Transformer模型，先预测电极尖端粗略热图，再通过细粒度坐标回归模块实现精确定位。为确保训练鲁棒性，使用匈牙利算法进行预测与真实位置间的最优匹配。实验结果表明，该方法在10μm以内定位准确率超过98%，5μm以内准确率超过89%，平均均方误差为2.52μm。
原文摘要 · Abstract (English)
In vivo image-guided multi-pipette patch-clamp is essential for studying cellular interactions and network dynamics in neuroscience. However, current procedures mainly rely on manual expertise, which limits accessibility and scalability. Robotic automation presents a promising solution, but achieving precise real-time detection of multiple pipettes remains a challenge. Existing methods focus on ex vivo experiments or single pipette use, making them inadequate for in vivo multi-pipette scenarios. To address these challenges, we propose a heatmap-augmented coarse-to-fine learning technique to facilitate multi-pipette real-time localisation for robot-assisted in vivo patch-clamp. More specifically, we introduce a Generative Adversarial Network (GAN)-based module to remove background noise and enhance pipette visibility. We then introduce a two-stage Transformer model that starts with predicting the coarse heatmap of the pipette tips, followed by the fine-grained coordination regression module for precise tip localisation. To ensure robust training, we use the Hungarian algorithm for optimal matching between the predicted and actual locations of tips. Experimental results demonstrate that our method achieved > 98% accuracy within 10 μm, and > 89% accuracy within 5 μm for the localisation of multi-pipette tips. The average MSE is 2.52 μm.
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