BACKGROUND Implantation accuracy of electrodes during neurosurgical interventions is necessary to ensure safety and efficacy. Typically, metrics are computed by visual inspection which is tedious, prone to inter-/intra-observer variation, and difficult to replicate across sites. NEW METHOD We propose an automated approach for computing implantation metrics and investigate potential sources of error. We focus on accuracy metrics commonly reported in the literature to validate our approach against metrics computed manually including entry point (EP) and target point (TP) localisation errors and angle differences between planned and implanted trajectories in 15 patients with a total of 158 stereoelectroencephalography (SEEG) electrodes. We evaluate the effect of line-of-best-fit approaches, EP definition and lateral versus Euclidean distance on metrics to provide recommendations for reporting implantation accuracy metrics. RESULTS We found no bias between manual and automated approaches for calculating accuracy metrics with limits of agreement of ±1 mm and ±1°. Automated metrics are robust to sources of errors including registration and electrode bending. We observe the highest error in EP deviations of μ = 0.25 mm when the post-implantation CT is used to define the point of entry. COMPARISON WITH EXISTING METHOD(S) We found no reports of automated approaches for quality assessment of SEEG electrode implantation. Neither the choice of metrics nor the possible errors that could occur have been investigated previously. CONCLUSIONS Our automated approach is useful to avoid human errors, unintentional bias and variation that may be introduced when manually computing metrics. Our work is relevant and timely to facilitate comparisons of studies reporting implantation accuracy.

中文翻译：

---

立体脑成像中准确性指标的自动化计算和分析。

背景技术在神经外科手术期间电极的植入精度对于确保安全性和有效性是必要的。通常，度量是通过目视检查来计算的，这很乏味，易于发生观察者之间/观察者内部的差异，并且难以在站点之间复制。新方法我们提出了一种自动方法来计算植入指标并调查潜在的错误来源。我们专注于文献中通常报道的准确性指标，以验证我们针对手动计算的指标（包括入口点（EP）和目标点（TP）定位误差以及计划和植入的轨迹之间的角度差异）进行手工计算的指标，共有15例患者进行了158例脑电图检查（ SEEG）电极。我们评估最适合方法的效果，EP定义以及横向与欧几里得距离的度量标准可为报告植入精度度量标准提供建议。结果我们发现，在手动和自动方法之间，在±1 mm和±1°的一致极限范围内计算精度指标时，没有偏差。自动化的度量标准对包括配准和电极弯曲在内的错误源均具有鲁棒性。当使用植入后CT来确定进入点时，我们观察到EP偏差的最大误差为μ= 0.25 mm。与现有方法的比较我们没有发现SEEG电极植入质量评估自动化方法的报道。度量标准的选择和可能发生的错误以前均未进行调查。结论我们的自动化方法可避免人为错误，手动计算指标时可能会引入的无意偏差和变化。我们的工作是相关且及时的，以促进比较报告植入准确性的研究。