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.

翻译：由于柔性微电极（FME）探针具有可形变的纤维状结构，且与关键生物组织存在相互作用，将其植入大脑皮层具有挑战性。为确保可靠性与安全性，需对植入过程进行仔细监控。本文基于机器人FME植入系统的显微相机，开发了一种基于图像的异常检测框架。该统一框架在四个检查点分别用于检查微针、FME探针、钩挂结果和植入点。利用现有的目标定位结果，从原始图像中提取对齐的感兴趣区域（ROI），并输入到预训练的视觉Transformer（ViT）中。考虑到任务的具体要求，我们提出了一种渐进粒度图像块特征采样方法，以解决不同位置处灵敏度与容错性之间的权衡问题。此外，我们从原始通用ViT特征中筛选出部分具有更高信噪比的特征通道，为每个特定场景提供更好的描述符。所提出方法的有效性已通过从我们植入系统收集的图像数据集得到验证。