用视觉检测技术保障柔性电极植入脑组织的可靠性与安全性
Visual Anomaly Detection for Reliable Robotic Implantation of Flexible Microelectrode Array
- 基于显微镜图像和预训练ViT，分四阶段检测植入过程异常
- 提出渐进式局部特征采样法，平衡不同位置的敏感性与容错性
- 筛选高信噪比特征通道，提升特定场景下的检测精度
柔性微电极（FME）植入脑皮层因探针细长可变形且与生物组织相互作用而极具挑战。为确保安全与可靠，需对植入过程进行严密监控。本文提出一种基于机器人系统显微相机图像的异常检测框架，在四个关键节点——微针、FME探针、钩挂结果及植入点——分别进行检测。利用已有目标定位结果，从原始图像中提取对齐的感兴趣区域（ROIs），输入预训练视觉变换器（ViT）。针对任务特性，提出渐进式粒度补丁特征采样方法，以解决不同位置敏感性与容错性之间的权衡问题。此外，从原始通用ViT特征中选取信噪比更高的部分特征通道，为每个具体场景提供更优描述符。所提方法在本系统采集的图像数据集上验证有效。
原文摘要 · Abstract (English)
Flexible microelectrode (FME) implantation into brain cortex is challenging due to the deformable fiber-like structure of FME probe and the interaction with critical bio-tissue. To ensure reliability and safety, the implantation process should be monitored carefully. This paper develops an image-based anomaly detection framework based on the microscopic cameras of the robotic FME implantation system. The unified framework is utilized at four checkpoints to check the micro-needle, FME probe, hooking result, and implantation point, respectively. Exploiting the existing object localization results, the aligned regions of interest (ROIs) are extracted from raw image and input to a pretrained vision transformer (ViT). Considering the task specifications, we propose a progressive granularity patch feature sampling method to address the sensitivity-tolerance trade-off issue at different locations. Moreover, we select a part of feature channels with higher signal-to-noise ratios from the raw general ViT features, to provide better descriptors for each specific scene. The effectiveness of the proposed methods is validated with the image datasets collected from our implantation system.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。